{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T10:15:59Z","timestamp":1784715359860,"version":"3.55.0"},"reference-count":251,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s00530-021-00884-5","type":"journal-article","created":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T06:04:25Z","timestamp":1642745065000},"page":"881-914","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["A holistic overview of deep learning approach in medical imaging"],"prefix":"10.1007","volume":"28","author":[{"given":"Rammah","family":"Yousef","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaurav","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nabhan","family":"Yousef","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5395-5335","authenticated-orcid":false,"given":"Manju","family":"Khari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"issue":"7553","key":"884_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"884_CR2","doi-asserted-by":"publisher","first-page":"14410","DOI":"10.1109\/ACCESS.2018.2807385","volume":"6","author":"N Akhtar","year":"2018","unstructured":"Akhtar, N., Mian, A.: Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access 6, 14410\u201314430 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2807385","journal-title":"IEEE Access"},{"issue":"January","key":"884_CR3","first-page":"3104","volume":"4","author":"I Sutskever","year":"2014","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 4(January), 3104\u20133112 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"22","key":"884_CR4","doi-asserted-by":"publisher","first-page":"2400","DOI":"10.1001\/jama.2012.5960","volume":"307","author":"R Smith-Bindman","year":"2012","unstructured":"Smith-Bindman, R., et al.: Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996\u20132010. JAMA 307(22), 2400\u20132409 (2012). https:\/\/doi.org\/10.1001\/jama.2012.5960","journal-title":"JAMA"},{"issue":"6","key":"884_CR5","doi-asserted-by":"publisher","first-page":"1511","DOI":"10.1148\/rg.316105207","volume":"31","author":"DL Rubin","year":"2011","unstructured":"Rubin, D.L.: Measuring and improving quality in radiology: meeting the challenge with informatics. Radiographics 31(6), 1511\u20131527 (2011). https:\/\/doi.org\/10.1148\/rg.316105207","journal-title":"Radiographics"},{"issue":"6","key":"884_CR6","doi-asserted-by":"publisher","first-page":"3576","DOI":"10.1007\/s00330-020-06672-5","volume":"30","author":"MP Recht","year":"2020","unstructured":"Recht, M.P., et al.: Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations. Eur. Radiol. 30(6), 3576\u20133584 (2020). https:\/\/doi.org\/10.1007\/s00330-020-06672-5","journal-title":"Eur. Radiol."},{"key":"884_CR7","doi-asserted-by":"crossref","unstructured":"Bosma, M., van Beuzekom, M., V\u00e4h\u00e4nen, S., Visser, J., Koffeman, E.: The influence of edge effects on the detection properties of Cadmium Telluride. In: 2011 IEEE Nuclear Science Symposium Conference Record IEEE, pp. 4812\u20134817 (2011)","DOI":"10.1109\/NSSMIC.2011.6154720"},{"issue":"December 2012","key":"884_CR8","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42(December 2012), 60\u201388 (2017). https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med. Image Anal."},{"issue":"4","key":"884_CR9","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989). https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput."},{"issue":"11","key":"884_CR10","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"884_CR11","unstructured":"Sutskever, I., Martens, J. and Hinton, G.: Generating text with recurrent neural networks. In: Proc. 28th Int. Conf. Mach. Learn. ICML 2011, pp. 1017\u20131024 (2011)"},{"key":"884_CR12","unstructured":"Balkanski, E., Rubinstein, A. and Singer, Y.: The power of optimization from samples. In: Advances in Neural Information Processing Systems, 2016, vol. 29. Available: https:\/\/proceedings.neurips.cc\/paper\/2016\/file\/c8758b517083196f05ac29810b924aca-Paper.pdf"},{"key":"884_CR13","doi-asserted-by":"crossref","unstructured":"Karpathy, A and Fei-Fei, L.: Deep Visual-Semantic Alignments for Generating Image Descriptions - Karpathy_Deep_Visual-Semantic_Alignments_2015_CVPR_paper.pdf. Cvpr (2015)","DOI":"10.1109\/CVPR.2015.7298932"},{"key":"884_CR14","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1109\/CVPR.2016.274","volume":"2016","author":"HC Shin","year":"2016","unstructured":"Shin, H.C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J., Summers, R.M.: Learning to read chest x-rays: recurrent neural cascade model for automated image annotation. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016, 2497\u20132506 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.274","journal-title":"Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit."},{"key":"884_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","volume":"73","author":"R Cui","year":"2019","unstructured":"Cui, R., Liu, M.: RNN-based longitudinal analysis for diagnosis of Alzheimer\u2019s disease. Comput. Med. Imaging Graph. 73, 1\u201310 (2019). https:\/\/doi.org\/10.1016\/j.compmedimag.2019.01.005","journal-title":"Comput. Med. Imaging Graph."},{"key":"884_CR16","doi-asserted-by":"publisher","DOI":"10.1117\/12.2549809","author":"J Zhang","year":"2020","unstructured":"Zhang, J., Zuo, H.: A deep RNN for CT image reconstruction. Proc. SPIE (2020). https:\/\/doi.org\/10.1117\/12.2549809","journal-title":"Proc. SPIE"},{"key":"884_CR17","volume-title":"Joint learning of motion estimation and segmentation for cardiac MR image sequences","author":"C Qin","year":"2018","unstructured":"Qin, C., et al.: Joint learning of motion estimation and segmentation for cardiac MR image sequences, vol. 11071. Springer International Publishing, LNCS (2018)"},{"key":"884_CR18","doi-asserted-by":"publisher","unstructured":"Ben-Cohen, A., Mechrez, R., Yedidia, N. and Greenspan, H.: Improving CNN training using disentanglement for liver lesion classification in CT. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 886\u2013889, https:\/\/doi.org\/10.1109\/EMBC.2019.8857465","DOI":"10.1109\/EMBC.2019.8857465"},{"issue":"3","key":"884_CR19","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1109\/TMI.2019.2933425","volume":"39","author":"H Liao","year":"2020","unstructured":"Liao, H., Lin, W.-A., Zhou, S.K., Luo, J.: ADN: artifact disentanglement network for unsupervised metal artifact reduction. IEEE Trans. Med. Imaging 39(3), 634\u2013643 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2933425","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR20","volume-title":"Unsupervised deformable registration for multi-modal images via disentangled representations","author":"C Qin","year":"2019","unstructured":"Qin, C., Shi, B., Liao, R., Mansi, T., Rueckert, D., Kamen, A.: Unsupervised deformable registration for multi-modal images via disentangled representations, vol. 11492. Springer International Publishing, LNCS (2019)"},{"key":"884_CR21","doi-asserted-by":"crossref","unstructured":"Creswell, A., Bharath, A. A.: Denoising adversarial autoencoders. IEEE transactions on neural networks and learning systems, 30(4), 968\u2013984 (2018)","DOI":"10.1109\/TNNLS.2018.2852738"},{"key":"884_CR22","volume-title":"Autoencoders","author":"WH Lopez Pinaya","year":"2019","unstructured":"Lopez Pinaya, W.H., Vieira, S., Garcia-Dias, R., Mechelli, A.: Autoencoders. Elsevier Inc. (2019)"},{"key":"884_CR23","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"884_CR24","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0147","author":"MA Ranzato","year":"2007","unstructured":"Ranzato, M.A., Poultney, C., Chopra, S., LeCun, Y.: Efficient learning of sparse representations with an energy-based model. Adv. Neural Inf. Process. Syst. (2007). https:\/\/doi.org\/10.7551\/mitpress\/7503.003.0147","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"884_CR25","unstructured":"Kingma, D.P. and Welling, M.: Auto-encoding variational bayes. In: 2nd International Conference of Learning Representation. ICLR 2014 - Conf. Track Proc., no. Ml, pp. 1\u201314 (2014)"},{"key":"884_CR26","unstructured":"Rifai, S., Vincent, P., Muller, X., Glorot, X. and Bengio, Y.: Contractive auto-encoders: explicit invariance during feature extraction. In: Proceeding 28th International Conference of Machine Learning. ICML 2011, no. 1, pp. 833\u2013840 (2011)"},{"key":"884_CR27","unstructured":"Li, C., Xu, K., Zhu, J. and Zhang, B.: Triple generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 2017-Decem, pp. 4089\u20134099 (2017)"},{"key":"884_CR28","unstructured":"Goodfellow, I. et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, 2014, vol. 27. Available: https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf"},{"key":"884_CR29","doi-asserted-by":"publisher","unstructured":"Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W. and Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: Proceeding - 30th IEEE Conference of Computer Vission Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2242\u20132251, (2017) https:\/\/doi.org\/10.1109\/CVPR.2017.241","DOI":"10.1109\/CVPR.2017.241"},{"key":"884_CR30","volume-title":"For Classification of Prostate Histopathology Whole-Slide Images","author":"JRBI Hacihaliloglu","year":"2018","unstructured":"Hacihaliloglu, J.R.B.I., Singer, E.A., Foran, D.J.: For Classification of Prostate Histopathology Whole-Slide Images, vol. 1. Springer International Publishing, Berlin (2018)"},{"key":"884_CR31","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.neucom.2018.11.111","volume":"392","author":"X Bi","year":"2020","unstructured":"Bi, X., Li, S., Xiao, B., Li, Y., Wang, G., Ma, X.: Computer aided Alzheimer\u2019s disease diagnosis by an unsupervised deep learning technology. Neurocomputing 392, 296\u2013304 (2020). https:\/\/doi.org\/10.1016\/j.neucom.2018.11.111","journal-title":"Neurocomputing"},{"key":"884_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00867","author":"CF Baumgartner","year":"2018","unstructured":"Baumgartner, C.F., Koch, L.M., Tezcan, K.C., Ang, J.X., Konukoglu, E.: Visual feature attribution using wasserstein GANs. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00867","journal-title":"Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit."},{"key":"884_CR33","unstructured":"Son, J., Park, S.J. and Jung, K.-H.: Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks (2017). Available: http:\/\/arxiv.org\/abs\/1706.09318"},{"key":"884_CR34","unstructured":"Dou, Q., et al.: PnP-AdaNet: plug-and-play adversarial domain adaptation network with a benchmark at cross-modality cardiac segmentation. CoRR, vol. abs\/1812.0, (2018). Available: http:\/\/arxiv.org\/abs\/1812.07907"},{"key":"884_CR35","unstructured":"Welander, P., Karlsson, S. and Eklund, A.: Generative adversarial networks for image-to-image translation on multi-contrast {MR} images - {A} comparison of CycleGAN and {UNIT}. CoRR vol. abs\/1806.0, (2018). Available: http:\/\/arxiv.org\/abs\/1806.07777"},{"key":"884_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101938","volume":"109","author":"S Kazeminia","year":"2020","unstructured":"Kazeminia, S., et al.: GANs for medical image analysis. Artif. Intell. Med. 109, 101938 (2020). https:\/\/doi.org\/10.1016\/j.artmed.2020.101938","journal-title":"Artif. Intell. Med."},{"issue":"1","key":"884_CR37","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/S0364-0213(85)80012-4","volume":"9","author":"DH Ackley","year":"1985","unstructured":"Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for boltzmann machines. Cogn. Sci. 9(1), 147\u2013169 (1985). https:\/\/doi.org\/10.1016\/S0364-0213(85)80012-4","journal-title":"Cogn. Sci."},{"issue":"2","key":"884_CR38","first-page":"220","volume":"4","author":"S Paul","year":"1986","unstructured":"Paul, S.: Information processing in dynamical systems: foundations of harmony theory. J. Jpn. Soc. Fuzzy Theory Syst. 4(2), 220\u2013228 (1986)","journal-title":"J. Jpn. Soc. Fuzzy Theory Syst."},{"issue":"5","key":"884_CR39","doi-asserted-by":"publisher","first-page":"1262","DOI":"10.1109\/TMI.2016.2526687","volume":"35","author":"G van Tulder","year":"2016","unstructured":"van Tulder, G., de Bruijne, M.: Combining generative and discriminative representation learning for lung CT analysis with convolutional restricted boltzmann machines. IEEE Trans. Med. Imaging 35(5), 1262\u20131272 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2526687","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"884_CR40","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527\u20131554 (2006). https:\/\/doi.org\/10.1162\/neco.2006.18.7.1527","journal-title":"Neural Comput."},{"key":"884_CR41","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.eswa.2017.05.073","volume":"86","author":"A Khatami","year":"2017","unstructured":"Khatami, A., Khosravi, A., Nguyen, T., Lim, C.P., Nahavandi, S.: Medical image analysis using wavelet transform and deep belief networks. Expert Syst. Appl. 86, 190\u2013198 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2017.05.073","journal-title":"Expert Syst. Appl."},{"key":"884_CR42","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00311-y","author":"AVN Reddy","year":"2020","unstructured":"Reddy, A.V.N., et al.: Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks. J. Big Data. (2020). https:\/\/doi.org\/10.1186\/s40537-020-00311-y","journal-title":"J. Big Data."},{"issue":"2","key":"884_CR43","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1007\/s10586-019-02999-x","volume":"23","author":"M Kaur","year":"2020","unstructured":"Kaur, M., Singh, D.: Fusion of medical images using deep belief networks. Cluster Comput. 23(2), 1439\u20131453 (2020). https:\/\/doi.org\/10.1007\/s10586-019-02999-x","journal-title":"Cluster Comput."},{"key":"884_CR44","doi-asserted-by":"crossref","unstructured":"Zhou, Z., et al.: Models genesis: generic autodidactic models for 3D medical image analysis. In: Medical Image Computing and Computer Assisted Intervention -- MICCAI 2019, pp. 384\u2013393 (2019)","DOI":"10.1007\/978-3-030-32251-9_42"},{"key":"884_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101746","volume":"64","author":"J Zhu","year":"2020","unstructured":"Zhu, J., Li, Y., Hu, Y., Ma, K., Zhou, S.K., Zheng, Y.: Rubik\u2019s Cube+: a self-supervised feature learning framework for 3D medical image analysis. Med. Image Anal. 64, 101746 (2020). https:\/\/doi.org\/10.1016\/j.media.2020.101746","journal-title":"Med. Image Anal."},{"key":"884_CR46","unstructured":"Azizi, S., et al.: Big self-supervised models advance medical image classification. no. 1, (2021). Available: http:\/\/arxiv.org\/abs\/2101.05224"},{"key":"884_CR47","doi-asserted-by":"crossref","unstructured":"Nie, D., Gao, Y., Wang, L. and Shen, D.: ASDNet: attention based semi-supervised deep networks for medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention -- MICCAI 2018, pp. 370\u2013378 (2018)","DOI":"10.1007\/978-3-030-00937-3_43"},{"key":"884_CR48","doi-asserted-by":"crossref","unstructured":"Bai, W., et al.: Semi-supervised learning for network-based cardiac MR image segmentation. In: Medical Image Computing and Computer-Assisted Intervention \u2212 MICCAI 2017, pp. 253\u2013260 (2017)","DOI":"10.1007\/978-3-319-66185-8_29"},{"issue":"11","key":"884_CR49","doi-asserted-by":"publisher","first-page":"3429","DOI":"10.1109\/TMI.2020.2995518","volume":"39","author":"Q Liu","year":"2020","unstructured":"Liu, Q., Yu, L., Luo, L., Dou, Q., Heng, P.A.: Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans. Med. Imaging 39(11), 3429\u20133440 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.2995518","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR50","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.media.2019.02.009","volume":"54","author":"H Kervadec","year":"2019","unstructured":"Kervadec, H., Dolz, J., Tang, M., Granger, E., Boykov, Y., Ben Ayed, I.: Constrained-CNN losses for weakly supervised segmentation. Med. Image Anal. 54, 88\u201399 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.02.009","journal-title":"Med. Image Anal."},{"key":"884_CR51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-13969-8_18","author":"X Wang","year":"2019","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray: hospital-scale chest x-ray database and benchmarks on weakly supervised classification and localization of common thorax diseases. Adv. Comput. Vis. Pattern Recognit. (2019). https:\/\/doi.org\/10.1007\/978-3-030-13969-8_18","journal-title":"Adv. Comput. Vis. Pattern Recognit."},{"key":"884_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101979","volume":"70","author":"G Shi","year":"2021","unstructured":"Shi, G., Xiao, L., Chen, Y., Zhou, S.K.: Marginal loss and exclusion loss for partially supervised multi-organ segmentation. Med. Image Anal. 70, 101979 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.101979","journal-title":"Med. Image Anal."},{"issue":"2","key":"884_CR53","doi-asserted-by":"publisher","first-page":"507","DOI":"10.3390\/make3020026","volume":"3","author":"HR Roth","year":"2021","unstructured":"Roth, H.R., Yang, D., Xu, Z., Wang, X., Xu, D.: Going to extremes: weakly supervised medical image segmentation. Mach. Learn. Knowl. Extr. 3(2), 507\u2013524 (2021). https:\/\/doi.org\/10.3390\/make3020026","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"884_CR54","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019). https:\/\/doi.org\/10.1016\/j.media.2019.01.010","journal-title":"Med. Image Anal."},{"key":"884_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2018.07.002","volume":"49","author":"Y Hu","year":"2018","unstructured":"Hu, Y., et al.: Weakly-supervised convolutional neural networks for multimodal image registration. Med. Image Anal. 49, 1\u201313 (2018). https:\/\/doi.org\/10.1016\/j.media.2018.07.002","journal-title":"Med. Image Anal."},{"key":"884_CR56","doi-asserted-by":"publisher","unstructured":"Quellec, G., Laniard, M., Cazuguel, G., Abr\u00e0moff, M.D., Cochener, B. and Roux, C.: Weakly supervised classification of medical images. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 110\u2013113 (2012) doi: https:\/\/doi.org\/10.1109\/ISBI.2012.6235496","DOI":"10.1109\/ISBI.2012.6235496"},{"issue":"4","key":"884_CR57","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1109\/TMI.2019.2946345","volume":"39","author":"W Abdullah Al","year":"2020","unstructured":"Abdullah Al, W., Yun, I.D.: Partial policy-based reinforcement learning for anatomical landmark localization in 3D medical images. IEEE Trans. Med. Imaging 39(4), 1245\u20131255 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2946345","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR58","doi-asserted-by":"publisher","unstructured":"Smith, R.L., Ackerley, I.M., Wells, K., Bartley, L., Paisey, S. and Marshall, C.: Reinforcement learning for object detection in PET imaging. In: 2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS\/MIC), pp. 1\u20134 (2019). doi: https:\/\/doi.org\/10.1109\/NSS\/MIC42101.2019.9060031","DOI":"10.1109\/NSS\/MIC42101.2019.9060031"},{"key":"884_CR59","doi-asserted-by":"publisher","unstructured":"Park, J., Jo, S., Lee, J., and Sun, W.: Color image classification on neuromorphic system using reinforcement learning. In: 2020 International Conference on Electronics, Information, and Communication (ICEIC), pp. 1\u20132 (2020). doi: https:\/\/doi.org\/10.1109\/ICEIC49074.2020.9051310","DOI":"10.1109\/ICEIC49074.2020.9051310"},{"issue":"10","key":"884_CR60","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010). https:\/\/doi.org\/10.1109\/TKDE.2009.191","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"5","key":"884_CR61","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285\u20131298 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2528162","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR62","doi-asserted-by":"publisher","first-page":"104603","DOI":"10.1109\/ACCESS.2020.2999816","volume":"8","author":"D Xue","year":"2020","unstructured":"Xue, D., et al.: An application of transfer learning and ensemble learning techniques for cervical histopathology image classification. IEEE Access 8, 104603\u2013104618 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2999816","journal-title":"IEEE Access"},{"issue":"5","key":"884_CR63","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh, N., et al.: Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans. Med. Imaging 35(5), 1299\u20131312 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2535302","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"884_CR64","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"BA Krizhevsky","year":"2012","unstructured":"Krizhevsky, B.A., Sutskever, I., Hinton, G.E.: Cnn\u5b9e\u9645\u8bad\u7ec3\u7684. Commun. ACM 60(6), 84\u201390 (2012)","journal-title":"Commun. ACM"},{"key":"884_CR65","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jocs.2018.11.008","volume":"30","author":"S Lu","year":"2019","unstructured":"Lu, S., Lu, Z., Zhang, Y.D.: Pathological brain detection based on AlexNet and transfer learning. J. Comput. Sci. 30, 41\u201347 (2019). https:\/\/doi.org\/10.1016\/j.jocs.2018.11.008","journal-title":"J. Comput. Sci."},{"key":"884_CR66","unstructured":"Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference of Learning Representation. ICLR 2015 - Conf. Track Proc., pp. 1\u201314 (2015)"},{"issue":"1","key":"884_CR67","doi-asserted-by":"publisher","first-page":"e1","DOI":"10.1002\/mp.13264","volume":"46","author":"B Sahiner","year":"2019","unstructured":"Sahiner, B., et al.: Deep learning in medical imaging and radiation therapy. Med. Phys. 46(1), e1\u2013e36 (2019). https:\/\/doi.org\/10.1002\/mp.13264","journal-title":"Med. Phys."},{"key":"884_CR68","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. and Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2016-Decem, pp. 2818\u20132826 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"884_CR69","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V. and Alemi, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning. In: 31st AAAI Conference of Artificial Intelligence. AAAI 2017, pp. 4278\u20134284 (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"issue":"1","key":"884_CR70","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1109\/JBHI.2019.2912659","volume":"24","author":"F Gao","year":"2020","unstructured":"Gao, F., Wu, T., Chu, X., Yoon, H., Xu, Y., Patel, B.: Deep residual inception encoder\u2013decoder network for medical imaging synthesis. IEEE J. Biomed. Heal. Informatics 24(1), 39\u201349 (2020). https:\/\/doi.org\/10.1109\/JBHI.2019.2912659","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"key":"884_CR71","doi-asserted-by":"crossref","unstructured":"A. {Szegedy, Christian and Liu, Wei and Jia, Yangqing and Sermanet, Pierre and Reed, Scott and Anguelov, Dragomir and Erhan, Dumitru and Vanhoucke, Vincent and Rabinovich, \u201c{Going Deeper With Convolutions}e,\u201d 2015, [Online]. Available: Szegedy, Christian, et al. %22Going deeper with convolutions.%22 Proceedings of the IEEE conference on computer vision and pattern recognition. 2015","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"884_CR72","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X.,S. Ren, S. and Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., vol. 2016-Decem, pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"884_CR73","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1007\/s10278-019-00221-3","volume":"32","author":"Q Wang","year":"2019","unstructured":"Wang, Q., Shen, F., Shen, L., Huang, J., Sheng, W.: Lung nodule detection in CT images using a raw patch-based convolutional neural network. J. Digit. Imaging 32(6), 971\u2013979 (2019). https:\/\/doi.org\/10.1007\/s10278-019-00221-3","journal-title":"J. Digit. Imaging"},{"key":"884_CR74","doi-asserted-by":"publisher","first-page":"v","DOI":"10.3233\/SHTI272","volume":"272","author":"J Mantas","year":"2020","unstructured":"Mantas, J., Hasman, A., Househ, M.S., Gallos, P., Zoulias, E.: Preface. Stud. Health Technol. Inform. 272, v (2020). https:\/\/doi.org\/10.3233\/SHTI272","journal-title":"Stud. Health Technol. Inform."},{"key":"884_CR75","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman Siddiquee, M. M., Tajbakhsh, N. and Liang, J.: UNet++: A nested U-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. pp. 3\u201311 (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"3","key":"884_CR76","doi-asserted-by":"publisher","first-page":"323","DOI":"10.6180\/jase.202106_24(3).0007","volume":"24","author":"E Mohammed Senan","year":"2021","unstructured":"Mohammed Senan, E., Waselallah Alsaade, F., Ibrahim Ahmed Al-mashhadani, M., Aldhyani, T.H.H., Hmoudal-Adhaileh, M.: Classification of histopathological images for early detection of breast cancer using deep learning. J. Appl. Sci. Eng. 24(3), 323\u2013329 (2021). https:\/\/doi.org\/10.6180\/jase.202106_24(3).0007","journal-title":"J. Appl. Sci. Eng."},{"key":"884_CR77","doi-asserted-by":"publisher","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L. and Weinberger, K.Q.: Densely connected convolutional networks. In: Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua. pp. 2261\u20132269 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"884_CR78","unstructured":"Mahmood, F., Yang, Z., Ashley, T. and Durr, N.J.: Multimodal Densenet (2018). Available: http:\/\/arxiv.org\/abs\/1811.07407"},{"key":"884_CR79","doi-asserted-by":"publisher","unstructured":"Xu, X., Lin, J., Tao, Y. and Wang, X.: An improved DenseNet method based on transfer learning for fundus medical images. In: 2018 7th International Conference on Digital Home (ICDH). pp. 137\u2013140 (2018). https:\/\/doi.org\/10.1109\/ICDH.2018.00033","DOI":"10.1109\/ICDH.2018.00033"},{"key":"884_CR80","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. and Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"884_CR81","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.engappai.2009.10.002","volume":"23","author":"M Forouzanfar","year":"2010","unstructured":"Forouzanfar, M., Forghani, N., Teshnehlab, M.: Parameter optimization of improved fuzzy c-means clustering algorithm for brain MR image segmentation. Eng. Appl. Artif. Intell. 23(2), 160\u2013168 (2010). https:\/\/doi.org\/10.1016\/j.engappai.2009.10.002","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"884_CR82","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L., Erickson, B.J.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30(4), 449\u2013459 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9983-4","journal-title":"J. Digit. Imaging"},{"key":"884_CR83","doi-asserted-by":"publisher","unstructured":"Milletari, F., Navab, N. and Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). pp. 565\u2013571 (2016). https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"884_CR84","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","volume":"35","author":"M Havaei","year":"2017","unstructured":"Havaei, M., et al.: Brain tumor segmentation with Deep Neural Networks. Med. Image Anal. 35, 18\u201331 (2017). https:\/\/doi.org\/10.1016\/j.media.2016.05.004","journal-title":"Med. Image Anal."},{"issue":"1","key":"884_CR85","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/s10462-020-09854-1","volume":"54","author":"S Asgari Taghanaki","year":"2021","unstructured":"Asgari Taghanaki, S., Abhishek, K., Cohen, J.P., Cohen-Adad, J., Hamarneh, G.: Deep semantic segmentation of natural and medical images: a review. Artif. Intell. Rev. 54(1), 137\u2013178 (2021). (Springer Netherlands)","journal-title":"Artif. Intell. Rev."},{"issue":"11","key":"884_CR86","doi-asserted-by":"publisher","first-page":"146","DOI":"10.4236\/jcc.2015.311023","volume":"03","author":"W Li","year":"2015","unstructured":"Li, W., Jia, F., Hu, Q.: Automatic segmentation of liver tumor in CT images with deep convolutional neural networks. J. Comput. Commun. 03(11), 146\u2013151 (2015). https:\/\/doi.org\/10.4236\/jcc.2015.311023","journal-title":"J. Comput. Commun."},{"key":"884_CR87","doi-asserted-by":"crossref","unstructured":"Dong, H., Yang, G., Liu, F., Mo, Y. and Guo Y.: Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. In: Medical Image Understanding and Analysis, pp. 506\u2013517 (2017)","DOI":"10.1007\/978-3-319-60964-5_44"},{"key":"884_CR88","unstructured":"Soltaninejad, M., Zhang, L., Lambrou, T., Allinson, N. and Ye, X.: Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network. (2017). Available: http:\/\/arxiv.org\/abs\/1704.08134"},{"key":"884_CR89","doi-asserted-by":"publisher","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","volume":"6","author":"J Ker","year":"2017","unstructured":"Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375\u20139379 (2017). https:\/\/doi.org\/10.1109\/ACCESS.2017.2788044","journal-title":"IEEE Access"},{"issue":"January","key":"884_CR90","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1016\/j.nicl.2017.06.016","volume":"15","author":"L Chen","year":"2017","unstructured":"Chen, L., Bentley, P., Rueckert, D.: Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin. 15(January), 633\u2013643 (2017). https:\/\/doi.org\/10.1016\/j.nicl.2017.06.016","journal-title":"NeuroImage Clin."},{"key":"884_CR91","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y. and Yu, J.: Brain tumor segmentation using an adversarial network. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. pp. 123\u2013132 (2018)","DOI":"10.1007\/978-3-319-75238-9_11"},{"key":"884_CR92","unstructured":"Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Jambawalikar, S. R.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. (2018). arXiv:1811.02629"},{"key":"884_CR93","doi-asserted-by":"crossref","unstructured":"Isensee, F., Kickingereder, P., Wick, W., Bendszus, M. and Maier-Hein, K. H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 287\u2013297 (2018)","DOI":"10.1007\/978-3-319-75238-9_25"},{"issue":"4","key":"884_CR94","doi-asserted-by":"publisher","first-page":"334","DOI":"10.18383\/j.tom.2016.00166","volume":"2","author":"P Korfiatis","year":"2016","unstructured":"Korfiatis, P., Kline, T.L., Erickson, B.J.: Automated segmentation of hyperintense regions in FLAIR MRI using deep learning. Tomography 2(4), 334\u2013340 (2016)","journal-title":"Tomography"},{"key":"884_CR95","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.media.2017.10.005","volume":"43","author":"M Liu","year":"2018","unstructured":"Liu, M., Zhang, J., Adeli, E., Shen, D.: Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43, 157\u2013168 (2018). https:\/\/doi.org\/10.1016\/j.media.2017.10.005","journal-title":"Med. Image Anal."},{"issue":"11","key":"884_CR96","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","volume":"35","author":"P Liskowski","year":"2016","unstructured":"Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369\u20132380 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2546227","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"884_CR97","doi-asserted-by":"publisher","first-page":"2732","DOI":"10.1364\/BOE.8.002732","volume":"8","author":"L Fang","year":"2017","unstructured":"Fang, L., Cunefare, D., Wang, C., Guymer, R.H., Li, S., Farsiu, S.: Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. Biomed. Opt. Express 8(5), 2732\u20132744 (2017). https:\/\/doi.org\/10.1364\/BOE.8.002732","journal-title":"Biomed. Opt. Express"},{"key":"884_CR98","doi-asserted-by":"crossref","unstructured":"Shankaranarayana, S.M., Ram, K., Mitra, K. and Sivaprakasam, M.: Joint optic disc and cup segmentation using fully convolutional and adversarial networks. In: Fetal, Infant and Ophthalmic Medical Image Analysis. pp. 168\u2013176 (2017)","DOI":"10.1007\/978-3-319-67561-9_19"},{"issue":"7","key":"884_CR99","doi-asserted-by":"publisher","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","volume":"37","author":"H Fu","year":"2018","unstructured":"Fu, H., Cheng, J., Xu, Y., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. IEEE Trans. Med. Imaging 37(7), 1597\u20131605 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2791488","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"24","key":"884_CR100","doi-asserted-by":"publisher","first-page":"8676","DOI":"10.1088\/1361-6560\/61\/24\/8676","volume":"61","author":"P Hu","year":"2016","unstructured":"Hu, P., Wu, F., Peng, J., Liang, P., Kong, D.: Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys. Med. Biol. 61(24), 8676\u20138698 (2016). https:\/\/doi.org\/10.1088\/1361-6560\/61\/24\/8676","journal-title":"Phys. Med. Biol."},{"key":"884_CR101","doi-asserted-by":"crossref","unstructured":"Ben-Cohen, A., Diamant, I., Klang, E., Amitai, M. and Greenspan, H.: Fully convolutional network for liver segmentation and lesions detection. In: Deep Learning and Data Labeling for Medical Applications, pp. 77\u201385 (2016)","DOI":"10.1007\/978-3-319-46976-8_9"},{"key":"884_CR102","doi-asserted-by":"crossref","unstructured":"Yang, D., et al.: Automatic liver segmentation using an adversarial image-to-image network. In: Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017. pp. 507\u2013515 (2017)","DOI":"10.1007\/978-3-319-66179-7_58"},{"issue":"March","key":"884_CR103","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep24454","volume":"6","author":"JZ Cheng","year":"2016","unstructured":"Cheng, J.Z., et al.: Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6(March), 1\u201313 (2016). https:\/\/doi.org\/10.1038\/srep24454","journal-title":"Sci. Rep."},{"issue":"April","key":"884_CR104","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ijmedinf.2018.06.003","volume":"117","author":"MA Al-antari","year":"2018","unstructured":"Al-antari, M.A., Al-masni, M.A., Choi, M.T., Han, S.M., Kim, T.S.: A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int. J. Med. Inform. 117(April), 44\u201354 (2018). https:\/\/doi.org\/10.1016\/j.ijmedinf.2018.06.003","journal-title":"Int. J. Med. Inform."},{"key":"884_CR105","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.procs.2018.01.104","volume":"127","author":"B Ait Skourt","year":"2018","unstructured":"Ait Skourt, B., El Hassani, A., Majda, A.: Lung CT image segmentation using deep neural networks. Procedia Comput. Sci. 127, 109\u2013113 (2018). https:\/\/doi.org\/10.1016\/j.procs.2018.01.104","journal-title":"Procedia Comput. Sci."},{"key":"884_CR106","unstructured":"Kalinovsky, A. and Kovalev, V.: Lung image segmentation using deep learning methods and convolutional neural networks. Int. Conf. Pattern Recognit. Inf. Process., no. July 2017, 21\u201324 (2016)"},{"issue":"8","key":"884_CR107","doi-asserted-by":"publisher","first-page":"2676","DOI":"10.1109\/TMI.2020.2994459","volume":"39","author":"S Roy","year":"2020","unstructured":"Roy, S., et al.: Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound. IEEE Trans. Med. Imaging 39(8), 2676\u20132687 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.2994459","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"884_CR108","doi-asserted-by":"publisher","first-page":"E166","DOI":"10.1148\/radiol.2020201874","volume":"296","author":"K Murphy","year":"2020","unstructured":"Murphy, K., et al.: COVID-19 on chest radiographs: a multireader evaluation of an artificial intelligence system. Radiology 296(3), E166\u2013E172 (2020). https:\/\/doi.org\/10.1148\/radiol.2020201874","journal-title":"Radiology"},{"issue":"4","key":"884_CR109","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/s10278-017-9978-1","volume":"30","author":"TL Kline","year":"2017","unstructured":"Kline, T.L., et al.: Performance of an artificial multi-observer deep neural network for fully automated segmentation of polycystic kidneys. J. Digit. Imaging 30(4), 442\u2013448 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9978-1","journal-title":"J. Digit. Imaging"},{"issue":"11","key":"884_CR110","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1007\/s11548-017-1649-7","volume":"12","author":"J Ma","year":"2017","unstructured":"Ma, J., Wu, F., Jiang, T., Zhao, Q., Kong, D.: Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 12(11), 1895\u20131910 (2017). https:\/\/doi.org\/10.1007\/s11548-017-1649-7","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"January","key":"884_CR111","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.01.006","volume":"63","author":"R Zhang","year":"2018","unstructured":"Zhang, R., Huang, L., Xia, W., Zhang, B., Qiu, B., Gao, X.: Multiple supervised residual network for osteosarcoma segmentation in CT images. Comput. Med. Imaging Graph. 63(January), 1\u20138 (2018). https:\/\/doi.org\/10.1016\/j.compmedimag.2018.01.006","journal-title":"Comput. Med. Imaging Graph."},{"issue":"8","key":"884_CR112","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.1109\/TBME.2016.2628401","volume":"64","author":"L Yu","year":"2017","unstructured":"Yu, L., Guo, Y., Wang, Y., Yu, J., Chen, P.: Segmentation of fetal left ventricle in echocardiographic sequences based on dynamic convolutional neural networks. IEEE Trans. Biomed. Eng. 64(8), 1886\u20131895 (2017). https:\/\/doi.org\/10.1109\/TBME.2016.2628401","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"884_CR113","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1007\/s11548-017-1567-8","volume":"12","author":"MH Jafari","year":"2017","unstructured":"Jafari, M.H., Nasr-Esfahani, E., Karimi, N., Soroushmehr, S.M.R., Samavi, S., Najarian, K.: Extraction of skin lesions from non-dermoscopic images for surgical excision of melanoma. Int. J. Comput. Assist. Radiol. Surg. 12(6), 1021\u20131030 (2017). https:\/\/doi.org\/10.1007\/s11548-017-1567-8","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"884_CR114","doi-asserted-by":"publisher","unstructured":"Yang, D., Zhang, S., Yan, Z., Tan, C., Li, K. and Metaxas, D.: Automated anatomical landmark detection ondistal femur surface using convolutional neural network. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). pp. 17\u201321 (2015). doi: https:\/\/doi.org\/10.1109\/ISBI.2015.7163806","DOI":"10.1109\/ISBI.2015.7163806"},{"key":"884_CR115","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.cmpb.2017.10.017","volume":"153","author":"JI Orlando","year":"2018","unstructured":"Orlando, J.I., Prokofyeva, E., del Fresno, M., Blaschko, M.B.: An ensemble deep learning based approach for red lesion detection in fundus images. Comput. Methods Programs Biomed. 153, 115\u2013127 (2018). https:\/\/doi.org\/10.1016\/j.cmpb.2017.10.017","journal-title":"Comput. Methods Programs Biomed."},{"key":"884_CR116","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.media.2017.08.006","volume":"42","author":"X Yang","year":"2017","unstructured":"Yang, X., et al.: Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI. Med. Image Anal. 42, 212\u2013227 (2017). https:\/\/doi.org\/10.1016\/j.media.2017.08.006","journal-title":"Med. Image Anal."},{"issue":"5","key":"884_CR117","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1109\/TMI.2016.2528129","volume":"35","author":"Q Dou","year":"2016","unstructured":"Dou, Q., et al.: Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans. Med. Imaging 35(5), 1182\u20131195 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2528129","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"884_CR118","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.5.3.036501","volume":"5","author":"K Yan","year":"2018","unstructured":"Yan, K., Wang, X., Lu, L., Summers, R.M.: DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J. Med. Imaging 5(3), 1\u201311 (2018). https:\/\/doi.org\/10.1117\/1.JMI.5.3.036501","journal-title":"J. Med. Imaging"},{"issue":"10","key":"884_CR119","doi-asserted-by":"publisher","first-page":"4753","DOI":"10.1109\/TIP.2017.2721106","volume":"26","author":"J Zhang","year":"2017","unstructured":"Zhang, J., Liu, M., Shen, D.: Detecting anatomical landmarks from limited medical imaging data using two-stage task-oriented deep neural networks. IEEE Trans. Image Process. 26(10), 4753\u20134764 (2017). https:\/\/doi.org\/10.1109\/TIP.2017.2721106","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"884_CR120","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1002\/jmri.25842","volume":"47","author":"T Nakao","year":"2018","unstructured":"Nakao, T., et al.: Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J. Magn. Reson. Imaging 47(4), 948\u2013953 (2018). https:\/\/doi.org\/10.1002\/jmri.25842","journal-title":"J. Magn. Reson. Imaging"},{"key":"884_CR121","doi-asserted-by":"crossref","unstructured":"Tsehay, Y., et al.: Biopsy-guided learning with deep convolutional neural networks for prostate cancer detection on multiparametric MRI Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Science, National Institute of Health, C. In: 2017 IEEE 14th Int. Symp. Biomed. Imaging (ISBI 2017), pp. 642\u2013645 (2017)","DOI":"10.1109\/ISBI.2017.7950602"},{"issue":"5","key":"884_CR122","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R.J., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196\u20131206 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2525803","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"884_CR123","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"AAA Setio","year":"2016","unstructured":"Setio, A.A.A., et al.: Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans. Med. Imaging 35(5), 1160\u20131169 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2536809","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"884_CR124","doi-asserted-by":"publisher","first-page":"E65","DOI":"10.1148\/radiol.2020200905","volume":"296","author":"L Li","year":"2020","unstructured":"Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., Cao, K.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 296(2), E65\u2013E71 (2020)","journal-title":"Radiology"},{"key":"884_CR125","doi-asserted-by":"publisher","DOI":"10.1007\/s42600-021-00151-6","author":"E Luz","year":"2021","unstructured":"Luz, E., et al.: Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images. Res. Biomed. Eng. (2021). https:\/\/doi.org\/10.1007\/s42600-021-00151-6","journal-title":"Res. Biomed. Eng."},{"issue":"3","key":"884_CR126","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1016\/j.bbe.2021.05.013","volume":"41","author":"SH Kassania","year":"2021","unstructured":"Kassania, S.H., Kassanib, P.H., Wesolowskic, M.J., Schneidera, K.A., Detersa, R.: Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach. Biocybern. Biomed. Eng. 41(3), 867\u2013879 (2021). https:\/\/doi.org\/10.1016\/j.bbe.2021.05.013","journal-title":"Biocybern. Biomed. Eng."},{"key":"884_CR127","unstructured":"Wang, D., Khosla, A., Gargeya, R., Irshad, H. and Beck, A.H.: Deep learning for identifying metastatic breast cancer. pp. 1\u20136 (2016). Available: http:\/\/arxiv.org\/abs\/1606.05718"},{"issue":"7","key":"884_CR128","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","volume":"64","author":"Q Dou","year":"2017","unstructured":"Dou, Q., Chen, H., Yu, L., Qin, J., Heng, P.A.: Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans. Biomed. Eng. 64(7), 1558\u20131567 (2017). https:\/\/doi.org\/10.1109\/TBME.2016.2613502","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"884_CR129","unstructured":"Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. pp. 3\u20139, (2017). Available: http:\/\/arxiv.org\/abs\/1711.05225"},{"issue":"5","key":"884_CR130","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1002\/mp.12134","volume":"44","author":"J Ma","year":"2017","unstructured":"Ma, J., Wu, F., Jiang, T., Zhu, J., Kong, D.: Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med. Phys. 44(5), 1678\u20131691 (2017). https:\/\/doi.org\/10.1002\/mp.12134","journal-title":"Med. Phys."},{"issue":"10","key":"884_CR131","doi-asserted-by":"publisher","first-page":"2138","DOI":"10.1109\/TMI.2017.2738612","volume":"36","author":"N Baka","year":"2017","unstructured":"Baka, N., Leenstra, S., Van Walsum, T.: Ultrasound aided vertebral level localization for lumbar surgery. IEEE Trans. Med. Imaging 36(10), 2138\u20132147 (2017). https:\/\/doi.org\/10.1109\/TMI.2017.2738612","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR132","doi-asserted-by":"publisher","DOI":"10.1117\/12.2254487","author":"V Alex","year":"2017","unstructured":"Alex, V., Safwan, P.K.M., Chennamsetty, S.S., Krishnamurthi, G.: Generative adversarial networks for brain lesion detection. Med. Imaging 2017 Image Process. (2017). https:\/\/doi.org\/10.1117\/12.2254487","journal-title":"Med. Imaging 2017 Image Process."},{"issue":"8","key":"884_CR133","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1109\/TMI.2019.2901398","volume":"38","author":"H Bogunovic","year":"2019","unstructured":"Bogunovic, H., et al.: RETOUCH: the retinal OCT fluid detection and segmentation benchmark and challenge. IEEE Trans. Med. Imaging 38(8), 1858\u20131874 (2019). https:\/\/doi.org\/10.1109\/TMI.2019.2901398","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"22","key":"884_CR134","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316(22), 2402\u20132410 (2016). https:\/\/doi.org\/10.1001\/jama.2016.17216","journal-title":"J. Am. Med. Assoc."},{"key":"884_CR135","doi-asserted-by":"publisher","DOI":"10.3390\/s19173722","author":"N Nasrullah","year":"2019","unstructured":"Nasrullah, N., Sang, J., Alam, M.S., Mateen, M., Cai, B., Hu, H.: Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors (2019). https:\/\/doi.org\/10.3390\/s19173722","journal-title":"Sensors"},{"key":"884_CR136","unstructured":"Domingues, I., Cardoso, J. S.: Mass detection on mammogram images: a first assessment of deep learning techniques. In: 19th Portuguese Conference on Pattern Recognition (RECPAD) (2013)"},{"key":"884_CR137","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/2061516","author":"Z Lai","year":"2018","unstructured":"Lai, Z., Deng, H.: Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. Comput. Intell. Neurosci. (2018). https:\/\/doi.org\/10.1155\/2018\/2061516","journal-title":"Comput. Intell. Neurosci."},{"key":"884_CR138","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2020.3042837","author":"B Xiao","year":"2021","unstructured":"Xiao, B., et al.: PAM-DenseNet: a deep convolutional neural network for computer-aided COVID-19 diagnosis. IEEE Trans. Cybern. (2021). https:\/\/doi.org\/10.1109\/TCYB.2020.3042837","journal-title":"IEEE Trans. Cybern."},{"issue":"4","key":"884_CR139","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/42.476112","volume":"14","author":"S-CB Lo","year":"1995","unstructured":"Lo, S.-C.B., Lou, S.-L.A., Lin, J.-S., Freedman, M.T., Chien, M.V., Mun, S.K.: Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans. Med. Imaging 14(4), 711\u2013718 (1995). https:\/\/doi.org\/10.1109\/42.476112","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR140","unstructured":"World Health Organization: Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children \/ World Health Organization Pneumonia Vaccine Trial Investigators\u2019 Group (2001). Available: http:\/\/www.who.int\/iris\/handle\/10665\/66956"},{"issue":"3","key":"884_CR141","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1109\/LGRS.2015.2513754","volume":"13","author":"J Ding","year":"2016","unstructured":"Ding, J., Chen, B., Liu, H., Huang, M.: Convolutional neural network with data augmentation for SAR target recognition. IEEE Geosci. Remote Sens. Lett. 13(3), 364\u2013368 (2016). https:\/\/doi.org\/10.1109\/LGRS.2015.2513754","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"884_CR142","unstructured":"Perez, L. and Wang, J.: The effectiveness of data augmentation in image classification using deep learning. CoRR, vol. abs\/1712.0 (2017). Available: http:\/\/arxiv.org\/abs\/1712.04621"},{"key":"884_CR143","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","volume":"321","author":"M Frid-Adar","year":"2018","unstructured":"Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321, 321\u2013331 (2018). https:\/\/doi.org\/10.1016\/j.neucom.2018.09.013","journal-title":"Neurocomputing"},{"key":"884_CR144","doi-asserted-by":"crossref","unstructured":"Li, R., et al.: Deep learning based imaging data completion for improved brain disease diagnosis. In: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2014. pp. 305\u2013312 (2014)","DOI":"10.1007\/978-3-319-10443-0_39"},{"key":"884_CR145","unstructured":"Hosseini-Asl, E., Gimel\u2019farb, G.L. and El-Baz, A.: Alzheimer\u2019s disease diagnostics by a deeply supervised adaptable 3D convolutional network. CoRR vol. abs\/1607.0 (2016). Available: http:\/\/arxiv.org\/abs\/1607.00556"},{"issue":"13","key":"884_CR146","doi-asserted-by":"publisher","first-page":"5200","DOI":"10.1167\/iovs.16-19964","volume":"57","author":"MD Abr\u00e0moff","year":"2016","unstructured":"Abr\u00e0moff, M.D., et al.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Vis. Sci. 57(13), 5200\u20135206 (2016). https:\/\/doi.org\/10.1167\/iovs.16-19964","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"issue":"5","key":"884_CR147","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","volume":"35","author":"M Anthimopoulos","year":"2016","unstructured":"Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., Mougiakakou, S.: Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1207\u20131216 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2535865","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"884_CR148","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1007\/s11548-017-1605-6","volume":"12","author":"A Nibali","year":"2017","unstructured":"Nibali, A., He, Z., Wollersheim, D.: Pulmonary nodule classification with deep residual networks. Int. J. Comput. Assist. Radiol. Surg. 12(10), 1799\u20131808 (2017). https:\/\/doi.org\/10.1007\/s11548-017-1605-6","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"884_CR149","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/JBHI.2016.2636929","volume":"21","author":"S Christodoulidis","year":"2017","unstructured":"Christodoulidis, S., Anthimopoulos, M., Ebner, L., Christe, A., Mougiakakou, S.: Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J. Biomed. Heal. Informatics 21(1), 76\u201384 (2017). https:\/\/doi.org\/10.1109\/JBHI.2016.2636929","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"issue":"March","key":"884_CR150","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejrad.2020.109041","volume":"128","author":"X Wu","year":"2020","unstructured":"Wu, X., et al.: Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicentre study. Eur. J. Radiol. 128(March), 1\u20139 (2020). https:\/\/doi.org\/10.1016\/j.ejrad.2020.109041","journal-title":"Eur. J. Radiol."},{"issue":"03","key":"884_CR151","doi-asserted-by":"publisher","first-page":"1141","DOI":"10.14299\/ijser.2020.03.02","volume":"11","author":"AA Farid","year":"2020","unstructured":"Farid, A.A., Selim, G.I., Khater, H.A.A.: A novel approach of CT images feature analysis and prediction to screen for corona virus disease (COVID-19). Int. J. Sci. Eng. Res. 11(03), 1141\u20131149 (2020). https:\/\/doi.org\/10.14299\/ijser.2020.03.02","journal-title":"Int. J. Sci. Eng. Res."},{"key":"884_CR152","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105532","volume":"194","author":"RM Pereira","year":"2020","unstructured":"Pereira, R.M., Bertolini, D., Teixeira, L.O., Silla, C.N., Costa, Y.M.G.: COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput. Methods Programs Biomed. 194, 105532 (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105532","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"884_CR153","doi-asserted-by":"publisher","DOI":"10.1117\/1.jmi.3.3.034501","volume":"3","author":"BQ Huynh","year":"2016","unstructured":"Huynh, B.Q., Li, H., Giger, M.L.: Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imaging 3(3), 034501 (2016). https:\/\/doi.org\/10.1117\/1.jmi.3.3.034501","journal-title":"J. Med. Imaging"},{"key":"884_CR154","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.compmedimag.2016.07.004","volume":"57","author":"W Sun","year":"2017","unstructured":"Sun, W., Tseng, T.L.B., Zhang, J., Qian, W.: Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data. Comput. Med. Imaging Graph. 57, 4\u20139 (2017). https:\/\/doi.org\/10.1016\/j.compmedimag.2016.07.004","journal-title":"Comput. Med. Imaging Graph."},{"key":"884_CR155","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.compmedimag.2019.05.001","volume":"75","author":"ZNK Swati","year":"2019","unstructured":"Swati, Z.N.K., et al.: Brain tumor classification for MR images using transfer learning and fine-tuning. Comput. Med. Imaging Graph. 75, 34\u201346 (2019). https:\/\/doi.org\/10.1016\/j.compmedimag.2019.05.001","journal-title":"Comput. Med. Imaging Graph."},{"key":"884_CR156","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","volume":"30","author":"M Sajjad","year":"2019","unstructured":"Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., Baik, S.W.: Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J. Comput. Sci. 30, 174\u2013182 (2019). https:\/\/doi.org\/10.1016\/j.jocs.2018.12.003","journal-title":"J. Comput. Sci."},{"issue":"June","key":"884_CR157","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2019.103345","volume":"111","author":"S Deepak","year":"2019","unstructured":"Deepak, S., Ameer, P.M.: Brain tumor classification using deep CNN features via transfer learning. Comput. Biol. Med. 111(June), 103345 (2019). https:\/\/doi.org\/10.1016\/j.compbiomed.2019.103345","journal-title":"Comput. Biol. Med."},{"key":"884_CR158","doi-asserted-by":"publisher","unstructured":"Afshar, P., Mohammadi, A. and Plataniotis, K. N.: Brain tumor type classification via capsule networks. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 3129\u20133133 (2018). https:\/\/doi.org\/10.1109\/ICIP.2018.8451379","DOI":"10.1109\/ICIP.2018.8451379"},{"key":"884_CR159","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.cmpb.2016.10.007","volume":"138","author":"XW Gao","year":"2017","unstructured":"Gao, X.W., Hui, R., Tian, Z.: Classification of CT brain images based on deep learning networks. Comput. Methods Programs Biomed. 138, 49\u201356 (2017). https:\/\/doi.org\/10.1016\/j.cmpb.2016.10.007","journal-title":"Comput. Methods Programs Biomed."},{"key":"884_CR160","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100391","volume":"20","author":"S Bharati","year":"2020","unstructured":"Bharati, S., Podder, P., Mondal, M.R.H.: Hybrid deep learning for detecting lung diseases from X-ray images. Inform. Med. Unlocked 20, 100391 (2020). https:\/\/doi.org\/10.1016\/j.imu.2020.100391","journal-title":"Inform. Med. Unlocked"},{"issue":"4","key":"884_CR161","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1002\/jmri.26721","volume":"50","author":"J Zhou","year":"2019","unstructured":"Zhou, J., et al.: Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. J. Magn. Reson. Imaging 50(4), 1144\u20131151 (2019). https:\/\/doi.org\/10.1002\/jmri.26721","journal-title":"J. Magn. Reson. Imaging"},{"key":"884_CR162","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.ultras.2016.08.004","volume":"72","author":"Q Zhang","year":"2016","unstructured":"Zhang, Q., et al.: Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 72, 150\u2013157 (2016). https:\/\/doi.org\/10.1016\/j.ultras.2016.08.004","journal-title":"Ultrasonics"},{"key":"884_CR163","first-page":"1571","volume":"2018","author":"C Yang","year":"2018","unstructured":"Yang, C., Rangarajan, A., Ranka, S.: Visual explanations from deep 3D Convolutional Neural Networks for Alzheimer\u2019s disease classification. AMIA \u2026 Annu. Symp. proceedings. AMIA Symp. 2018, 1571\u20131580 (2018)","journal-title":"AMIA \u2026 Annu. Symp. proceedings. AMIA Symp."},{"issue":"November","key":"884_CR164","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.lungcan.2018.11.001","volume":"126","author":"M Schwyzer","year":"2018","unstructured":"Schwyzer, M., et al.: Automated detection of lung cancer at ultralow dose PET\/CT by deep neural networks\u2014initial results. Lung Cancer 126(November), 170\u2013173 (2018). https:\/\/doi.org\/10.1016\/j.lungcan.2018.11.001","journal-title":"Lung Cancer"},{"key":"884_CR165","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.patcog.2018.03.032","volume":"81","author":"AO de Carvalho Filho","year":"2018","unstructured":"de Carvalho Filho, A.O., Silva, A.C., de Paiva, A.C., Nunes, R.A., Gattass, M.: Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network. Pattern Recognit. 81, 200\u2013212 (2018). https:\/\/doi.org\/10.1016\/j.patcog.2018.03.032","journal-title":"Pattern Recognit."},{"key":"884_CR166","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","volume":"61","author":"W Shen","year":"2017","unstructured":"Shen, W., et al.: Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification. Pattern Recognit. 61, 663\u2013673 (2017). https:\/\/doi.org\/10.1016\/j.patcog.2016.05.029","journal-title":"Pattern Recognit."},{"issue":"April","key":"884_CR167","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk, T., Talo, M., Yildirim, E.A., Baloglu, U.B., Yildirim, O., Rajendra Acharya, U.: Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121(April), 103792 (2020). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103792","journal-title":"Comput. Biol. Med."},{"issue":"April","key":"884_CR168","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.109761","volume":"140","author":"F Ucar","year":"2020","unstructured":"Ucar, F., Korkmaz, D.: COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med. Hypotheses 140(April), 109761 (2020). https:\/\/doi.org\/10.1016\/j.mehy.2020.109761","journal-title":"Med. Hypotheses"},{"issue":"1","key":"884_CR169","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-76550-z","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang, L., Lin, Z.Q., Wong, A.: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. Sci. Rep. 10(1), 1\u201312 (2020). https:\/\/doi.org\/10.1038\/s41598-020-76550-z","journal-title":"Sci. Rep."},{"key":"884_CR170","unstructured":"Rezvantalab, A., Safigholi, H. and Karimijeshni, S.: Dermatologist level dermoscopy skin cancer classification using different deep learning convolutional neural networks algorithms. (2018). Available: http:\/\/arxiv.org\/abs\/1810.10348"},{"issue":"8","key":"884_CR171","doi-asserted-by":"publisher","first-page":"9909","DOI":"10.1007\/s11042-018-5714-1","volume":"77","author":"UO Dorj","year":"2018","unstructured":"Dorj, U.O., Lee, K.K., Choi, J.Y., Lee, M.: The skin cancer classification using deep convolutional neural network. Multimed. Tools Appl. 77(8), 9909\u20139924 (2018). https:\/\/doi.org\/10.1007\/s11042-018-5714-1","journal-title":"Multimed. Tools Appl."},{"issue":"7639","key":"884_CR172","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115\u2013118 (2017). https:\/\/doi.org\/10.1038\/nature21056","journal-title":"Nature"},{"key":"884_CR173","doi-asserted-by":"publisher","unstructured":"Awais, M., Muller, H., Tang, T. B. and Meriaudeau, F.: Classification of SD-OCT images using a deep learning approach. In: Proc. 2017 IEEE Int. Conf. Signal Image Process. Appl. ICSIPA 2017, vol. c, pp. 489\u2013492 (2017). https:\/\/doi.org\/10.1109\/ICSIPA.2017.8120661","DOI":"10.1109\/ICSIPA.2017.8120661"},{"issue":"22","key":"884_CR174","doi-asserted-by":"publisher","first-page":"2211","DOI":"10.1001\/jama.2017.18152","volume":"318","author":"DSW Ting","year":"2017","unstructured":"Ting, D.S.W., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. J. Am. Med. Assoc. 318(22), 2211\u20132223 (2017). https:\/\/doi.org\/10.1001\/jama.2017.18152","journal-title":"J. Am. Med. Assoc."},{"key":"884_CR175","doi-asserted-by":"publisher","unstructured":"Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J. and Greenspan, H.: Synthetic data augmentation using GAN for improved liver lesion classification. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 289\u2013293 (2018). https:\/\/doi.org\/10.1109\/ISBI.2018.8363576","DOI":"10.1109\/ISBI.2018.8363576"},{"key":"884_CR176","doi-asserted-by":"crossref","unstructured":"Krebs, J., et al.: Robust non-rigid registration through agent-based action learning. In: Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017. pp. 344\u2013352 (2017)","DOI":"10.1007\/978-3-319-66182-7_40"},{"key":"884_CR177","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neuroimage.2017.07.008","volume":"158","author":"X Yang","year":"2017","unstructured":"Yang, X., Kwitt, R., Styner, M., Niethammer, M.: Quicksilver: fast predictive image registration\u2014a deep learning approach. Neuroimage 158, 378\u2013396 (2017). https:\/\/doi.org\/10.1016\/j.neuroimage.2017.07.008","journal-title":"Neuroimage"},{"key":"884_CR178","doi-asserted-by":"crossref","unstructured":"Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B. P. F., I\u0161gum, I. and Staring, M.: Nonrigid image registration using multi-scale 3D Convolutional Neural Networks. In: Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017, pp. 232\u2013239 (2017)","DOI":"10.1007\/978-3-319-66182-7_27"},{"key":"884_CR179","doi-asserted-by":"crossref","unstructured":"Wu, G., Kim, M., Wang, Q., Gao, Y., Liao, S. and Shen, D.: Unsupervised deep feature learning for deformable registration of MR brain images. In: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2013, pp. 649\u2013656 (2013)","DOI":"10.1007\/978-3-642-40763-5_80"},{"issue":"7","key":"884_CR180","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1109\/TBME.2015.2496253","volume":"63","author":"G Wu","year":"2016","unstructured":"Wu, G., Kim, M., Wang, Q., Munsell, B.C., Shen, D.: Scalable high-performance image registration framework by unsupervised deep feature representations learning. IEEE Trans. Biomed. Eng. 63(7), 1505\u20131516 (2016). https:\/\/doi.org\/10.1109\/TBME.2015.2496253","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"5","key":"884_CR181","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1109\/TMI.2016.2521800","volume":"35","author":"S Miao","year":"2016","unstructured":"Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D\/3D registration. IEEE Trans. Med. Imaging 35(5), 1352\u20131363 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2521800","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR182","doi-asserted-by":"publisher","unstructured":"de Vos, B.D., Berendsen, F. F., Viergever, M. A., Staring, M., I\u0161gum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10553 LNCS, pp. 204\u2013212 (2017). doi: https:\/\/doi.org\/10.1007\/978-3-319-67558-9_24","DOI":"10.1007\/978-3-319-67558-9_24"},{"key":"884_CR183","volume-title":"Deformable MRI-ultrasound registration using 3D convolutional neural network","author":"L Sun","year":"2018","unstructured":"Sun, L., Zhang, S.: Deformable MRI-ultrasound registration using 3D convolutional neural network, vol. 11042. Springer International Publishing, LNCS (2018)"},{"key":"884_CR184","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106335","volume":"93","author":"Y Chen","year":"2020","unstructured":"Chen, Y., He, F., Li, H., Zhang, D., Wu, Y.: A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration. Appl. Soft Comput. J. 93, 106335 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2020.106335","journal-title":"Appl. Soft Comput. J."},{"key":"884_CR185","doi-asserted-by":"publisher","first-page":"8455","DOI":"10.1109\/CVPR.2019.00866","volume":"2019","author":"M Niethammer","year":"2019","unstructured":"Niethammer, M., Kwitt, R., Vialard, F.X.: Metric learning for image registration. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2019, 8455\u20138464 (2019). https:\/\/doi.org\/10.1109\/CVPR.2019.00866","journal-title":"Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit."},{"issue":"7","key":"884_CR186","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/B978-0-12-810408-8.00015-8","volume":"63","author":"S Wang","year":"2017","unstructured":"Wang, S., Kim, M., Wu, G., Shen, D.: Scalable high performance image registration framework by unsupervised deep feature representations learning. Deep Learn. Med. Image Anal. 63(7), 245\u2013269 (2017). https:\/\/doi.org\/10.1016\/B978-0-12-810408-8.00015-8","journal-title":"Deep Learn. Med. Image Anal."},{"issue":"10","key":"884_CR187","doi-asserted-by":"publisher","first-page":"e360","DOI":"10.1002\/mp.12344","volume":"44","author":"E Kang","year":"2017","unstructured":"Kang, E., Min, J., Ye, J.C.: A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction. Med. Phys. 44(10), e360\u2013e375 (2017). https:\/\/doi.org\/10.1002\/mp.12344","journal-title":"Med. Phys."},{"key":"884_CR188","doi-asserted-by":"publisher","unstructured":"Abanovi\u00e8, E., Stankevi\u00e8ius, G. and Matuzevi\u00e8ius, D.: Deep Neural Network-based feature descriptor for retinal image registration. In: 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE), pp. 1\u20134 (2018)https:\/\/doi.org\/10.1109\/AIEEE.2018.8592033","DOI":"10.1109\/AIEEE.2018.8592033"},{"issue":"3","key":"884_CR189","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/s11548-018-1875-7","volume":"14","author":"G Haskins","year":"2019","unstructured":"Haskins, G., et al.: Learning deep similarity metric for 3D MR\u2013TRUS image registration. Int. J. Comput. Assist. Radiol. Surg. 14(3), 417\u2013425 (2019). https:\/\/doi.org\/10.1007\/s11548-018-1875-7","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"12","key":"884_CR190","doi-asserted-by":"publisher","first-page":"5799","DOI":"10.1118\/1.3013555","volume":"35","author":"ML Giger","year":"2008","unstructured":"Giger, M.L., Chan, H.-P., Boone, J.: Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Med. Phys. 35(12), 5799\u20135820 (2008). https:\/\/doi.org\/10.1118\/1.3013555","journal-title":"Med. Phys."},{"issue":"1","key":"884_CR191","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1146\/annurev-bioeng-071812-152416","volume":"15","author":"ML Giger","year":"2013","unstructured":"Giger, M.L., Karssemeijer, N., Schnabel, J.A.: Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu. Rev. Biomed. Eng. 15(1), 327\u2013357 (2013). https:\/\/doi.org\/10.1146\/annurev-bioeng-071812-152416","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"1","key":"884_CR192","doi-asserted-by":"publisher","first-page":"16012","DOI":"10.1038\/npjbcancer.2016.12","volume":"2","author":"H Li","year":"2016","unstructured":"Li, H., et al.: Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA\/TCIA data set. NPJ Breast Cancer 2(1), 16012 (2016). https:\/\/doi.org\/10.1038\/npjbcancer.2016.12","journal-title":"NPJ Breast Cancer"},{"issue":"4","key":"884_CR193","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.2.4.041007","volume":"2","author":"W Guo","year":"2015","unstructured":"Guo, W., et al.: Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data. J. Med. Imaging 2(4), 1\u201312 (2015). https:\/\/doi.org\/10.1117\/1.JMI.2.4.041007","journal-title":"J. Med. Imaging"},{"issue":"2","key":"884_CR194","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1148\/radiol.2016152110","volume":"281","author":"H Li","year":"2016","unstructured":"Li, H., et al.: MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 Gene assays. Radiology 281(2), 382\u2013391 (2016). https:\/\/doi.org\/10.1148\/radiol.2016152110","journal-title":"Radiology"},{"issue":"3","key":"884_CR195","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/BF03168597","volume":"10","author":"S Katsuragawa","year":"1997","unstructured":"Katsuragawa, S., Doi, K., MacMahon, H., Monnier-Cholley, L., Ishida, T., Kobayashi, T.: Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks. J. Digit. Imaging 10(3), 108\u2013114 (1997). https:\/\/doi.org\/10.1007\/BF03168597","journal-title":"J. Digit. Imaging"},{"issue":"4","key":"884_CR196","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s10278-017-0028-9","volume":"31","author":"GB Kim","year":"2018","unstructured":"Kim, G.B., et al.: Comparison of shallow and deep learning methods on classifying the regional pattern of diffuse lung disease. J. Digit. Imaging 31(4), 415\u2013424 (2018). https:\/\/doi.org\/10.1007\/s10278-017-0028-9","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"884_CR197","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.5.1.014503","volume":"5","author":"NO Antropova","year":"2018","unstructured":"Antropova, N.O., Abe, H., Giger, M.L.: Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks. J. Med. Imaging 5(1), 1\u20136 (2018). https:\/\/doi.org\/10.1117\/1.JMI.5.1.014503","journal-title":"J. Med. Imaging"},{"issue":"1","key":"884_CR198","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1002\/mp.12683","volume":"45","author":"AA Mohamed","year":"2018","unstructured":"Mohamed, A.A., Berg, W.A., Peng, H., Luo, Y., Jankowitz, R.C., Wu, S.: A deep learning method for classifying mammographic breast density categories. Med. Phys. 45(1), 314\u2013321 (2018). https:\/\/doi.org\/10.1002\/mp.12683","journal-title":"Med. Phys."},{"issue":"3","key":"884_CR199","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1002\/mp.12763","volume":"45","author":"J Lee","year":"2018","unstructured":"Lee, J., Nishikawa, R.M.: Automated mammographic breast density estimation using a fully convolutional network. Med. Phys. 45(3), 1178\u20131190 (2018). https:\/\/doi.org\/10.1002\/mp.12763","journal-title":"Med. Phys."},{"issue":"10","key":"884_CR200","doi-asserted-by":"publisher","first-page":"5162","DOI":"10.1002\/mp.12453","volume":"44","author":"N Antropova","year":"2017","unstructured":"Antropova, N., Huynh, B.Q., Giger, M.L.: A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med. Phys. 44(10), 5162\u20135171 (2017). https:\/\/doi.org\/10.1002\/mp.12453","journal-title":"Med. Phys."},{"issue":"23","key":"884_CR201","doi-asserted-by":"publisher","first-page":"8894","DOI":"10.1088\/1361-6560\/aa93d4","volume":"62","author":"RK Samala","year":"2017","unstructured":"Samala, R.K., Chan, H.-P., Hadjiiski, L.M., Helvie, M.A., Cha, K.H., Richter, C.D.: Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms. Phys. Med. Biol. 62(23), 8894\u20138908 (2017). https:\/\/doi.org\/10.1088\/1361-6560\/aa93d4","journal-title":"Phys. Med. Biol."},{"issue":"3","key":"884_CR202","doi-asserted-by":"publisher","first-page":"1017","DOI":"10.1002\/mp.12110","volume":"44","author":"T Kooi","year":"2017","unstructured":"Kooi, T., van Ginneken, B., Karssemeijer, N., den Heeten, A.: Discriminating solitary cysts from soft tissue lesions in mammography using a pretrained deep convolutional neural network. Med. Phys. 44(3), 1017\u20131027 (2017). https:\/\/doi.org\/10.1002\/mp.12110","journal-title":"Med. Phys."},{"issue":"January","key":"884_CR203","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.jbi.2018.01.005","volume":"79","author":"A Masood","year":"2018","unstructured":"Masood, A., et al.: Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images. J. Biomed. Inform. 79(January), 117\u2013128 (2018). https:\/\/doi.org\/10.1016\/j.jbi.2018.01.005","journal-title":"J. Biomed. Inform."},{"issue":"2","key":"884_CR204","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1164\/rccm.201705-0860OC","volume":"197","author":"G Gonzalez","year":"2018","unstructured":"Gonzalez, G., et al.: Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am. J. Respir. Crit. Care Med. 197(2), 193\u2013203 (2018). https:\/\/doi.org\/10.1164\/rccm.201705-0860OC","journal-title":"Am. J. Respir. Crit. Care Med."},{"issue":"1","key":"884_CR205","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-10649-8","volume":"7","author":"J Lao","year":"2017","unstructured":"Lao, J., et al.: A deep learning-based radiomics model for prediction of survival in Glioblastoma Multiforme. Sci. Rep. 7(1), 1\u20138 (2017). https:\/\/doi.org\/10.1038\/s41598-017-10649-8","journal-title":"Sci. Rep."},{"issue":"11","key":"884_CR206","doi-asserted-by":"publisher","first-page":"5814","DOI":"10.1002\/mp.12510","volume":"44","author":"SS Garapati","year":"2017","unstructured":"Garapati, S.S., et al.: Urinary bladder cancer staging in CT urography using machine learning. Med. Phys. 44(11), 5814\u20135823 (2017). https:\/\/doi.org\/10.1002\/mp.12510","journal-title":"Med. Phys."},{"issue":"6","key":"884_CR207","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0179790","volume":"12","author":"H Takahashi","year":"2017","unstructured":"Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., Kawashima, H.: Applying artificial intelligence to disease staging: deep learning for improved staging of diabetic retinopathy. PLoS\u00a0One 12(6), 1\u201311 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0179790","journal-title":"PLoS\u00a0One"},{"issue":"10221","key":"884_CR208","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1016\/S0140-6736(19)32998-8","volume":"395","author":"O-J Skrede","year":"2020","unstructured":"Skrede, O.-J., et al.: Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet 395(10221), 350\u2013360 (2020). https:\/\/doi.org\/10.1016\/S0140-6736(19)32998-8","journal-title":"Lancet"},{"issue":"6","key":"884_CR209","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.1002\/hep.31207","volume":"72","author":"C Saillard","year":"2020","unstructured":"Saillard, C., et al.: Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology 72(6), 2000\u20132013 (2020). https:\/\/doi.org\/10.1002\/hep.31207","journal-title":"Hepatology"},{"key":"884_CR210","unstructured":"\u201cNo Title,\u201d https:\/\/www.who.int\/news-room\/fact-sheets\/detail. 2019, [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail"},{"issue":"2","key":"884_CR211","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200152","volume":"2","author":"S Simpson","year":"2020","unstructured":"Simpson, S., et al.: Radiological society of North America expert consensus document on reporting chest CT findings related to COVID-19: endorsed by the society of thoracic radiology, the American College of Radiology, and RSNA. Radiol. Cardiothorac. Imaging 2(2), e200152 (2020). https:\/\/doi.org\/10.1148\/ryct.2020200152","journal-title":"Radiol. Cardiothorac. Imaging"},{"issue":"04","key":"884_CR212","doi-asserted-by":"publisher","first-page":"82","DOI":"10.4236\/ss.2020.114011","volume":"11","author":"A Mahmood","year":"2020","unstructured":"Mahmood, A., Gajula, C., Gajula, P.: COVID 19 diagnostic tests: a study of 12,270 patients to determine which test offers the most beneficial results. Surg. Sci. 11(04), 82\u201388 (2020). https:\/\/doi.org\/10.4236\/ss.2020.114011","journal-title":"Surg. Sci."},{"issue":"7","key":"884_CR213","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1002\/jum.15284","volume":"39","author":"G Soldati","year":"2020","unstructured":"Soldati, G., et al.: Is there a role for lung ultrasound during the COVID-19 pandemic? J. Ultrasound Med. 39(7), 1459\u20131462 (2020). https:\/\/doi.org\/10.1002\/jum.15284","journal-title":"J. Ultrasound Med."},{"key":"884_CR214","doi-asserted-by":"publisher","unstructured":"Butt, C., Gill, J., Chun, D. et al.: RETRACTED ARTICLE: Deep learning system to screen coronavirus disease 2019 pneumonia. Appl Intell (2020). https:\/\/doi.org\/10.1007\/s10489-020-01714-3","DOI":"10.1007\/s10489-020-01714-3"},{"key":"884_CR215","doi-asserted-by":"publisher","unstructured":"Wang, B., Wu, Z., Khan, Z. U., Liu, C. and Zhu, M.: Deep convolutional neural network with segmentation techniques for chest x-ray analysis. In: 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA). pp. 1212\u20131216 (2019). https:\/\/doi.org\/10.1109\/ICIEA.2019.8834117","DOI":"10.1109\/ICIEA.2019.8834117"},{"key":"884_CR216","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103795","volume":"121","author":"AA Ardakani","year":"2020","unstructured":"Ardakani, A.A., Kanafi, A.R., Acharya, U.R., Khadem, N., Mohammadi, A.: Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput. Biol. Med. 121, 103795 (2020). https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103795","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"884_CR217","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1117\/1.JMI.4.4.041304","volume":"4","author":"H Li","year":"2017","unstructured":"Li, H., Giger, M.L., Huynh, B.Q., Antropova, N.O.: Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. J. Med. Imaging 4(4), 1\u20136 (2017). https:\/\/doi.org\/10.1117\/1.JMI.4.4.041304","journal-title":"J. Med. Imaging"},{"key":"884_CR218","doi-asserted-by":"crossref","unstructured":"Roth, H.R., et al.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. pp. 556\u2013564 (2015)","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"884_CR219","unstructured":"Asperti, A. and Mastronardo, C.: The effectiveness of data augmentation for detection of gastrointestinal diseases from endoscopical images. CoRR, vol. abs\/1712.0 (2017). Available: http:\/\/arxiv.org\/abs\/1712.03689"},{"issue":"4","key":"884_CR220","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/TMI.2016.2640180","volume":"36","author":"A Pezeshk","year":"2017","unstructured":"Pezeshk, A., Petrick, N., Chen, W., Sahiner, B.: Seamless lesion insertion for data augmentation in CAD training. IEEE Trans. Med. Imaging 36(4), 1005\u20131015 (2017). https:\/\/doi.org\/10.1109\/TMI.2016.2640180","journal-title":"IEEE Trans. Med. Imaging"},{"key":"884_CR221","doi-asserted-by":"crossref","unstructured":"Zhang, C., Tavanapong, W., Wong, J., de Groen, P.C. and Oh, J.: Real data augmentation for medical image classification. In: Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. pp. 67\u201376 (2017)","DOI":"10.1007\/978-3-319-67534-3_8"},{"issue":"1","key":"884_CR222","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1038\/s41598-018-19426-7","volume":"8","author":"X Yang","year":"2018","unstructured":"Yang, X., et al.: Low-dose x-ray tomography through a deep convolutional neural network. Sci. Rep. 8(1), 2575 (2018). https:\/\/doi.org\/10.1038\/s41598-018-19426-7","journal-title":"Sci. Rep."},{"issue":"2","key":"884_CR223","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1364\/BOE.8.000679","volume":"8","author":"H Chen","year":"2017","unstructured":"Chen, H., et al.: Low-dose CT via convolutional neural network. Biomed. Opt. Express 8(2), 679\u2013694 (2017). https:\/\/doi.org\/10.1364\/BOE.8.000679","journal-title":"Biomed. Opt. Express"},{"issue":"9","key":"884_CR224","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0184667","volume":"12","author":"J Cui","year":"2017","unstructured":"Cui, J., Liu, X., Wang, Y., Liu, H.: Deep reconstruction model for dynamic PET images. PLoS\u00a0One 12(9), 1\u201321 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0184667","journal-title":"PLoS\u00a0One"},{"issue":"4","key":"884_CR225","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/s10278-017-9976-3","volume":"30","author":"MD Kohli","year":"2017","unstructured":"Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning\u2014whitepaper from the 2016 C-MIMI meeting dataset session. J. Digit. Imaging 30(4), 392\u2013399 (2017). https:\/\/doi.org\/10.1007\/s10278-017-9976-3","journal-title":"J. Digit. Imaging"},{"issue":"1","key":"884_CR226","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1148\/radiol.2020192224","volume":"295","author":"MJ Willemink","year":"2020","unstructured":"Willemink, M.J., et al.: Preparing medical imaging data for machine learning. Radiology 295(1), 4\u201315 (2020). https:\/\/doi.org\/10.1148\/radiol.2020192224","journal-title":"Radiology"},{"issue":"3","key":"884_CR227","doi-asserted-by":"publisher","first-page":"99540","DOI":"10.1109\/ACCESS.2019.2929365","volume":"7","author":"F Altaf","year":"2019","unstructured":"Altaf, F., Islam, S.M.S., Akhtar, N., Janjua, N.K.: Going deep in medical image analysis: concepts, methods, challenges, and future directions. IEEE Access 7(3), 99540\u201399572 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2929365","journal-title":"IEEE Access"},{"issue":"10","key":"884_CR228","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2015). https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"884_CR229","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","volume":"74","author":"RC Petersen","year":"2010","unstructured":"Petersen, R.C., et al.: Alzheimer\u2019s disease neuroimaging initiative (ADNI). Neurology 74(3), 201\u2013209 (2010). https:\/\/doi.org\/10.1212\/WNL.0b013e3181cb3e25","journal-title":"Neurology"},{"issue":"3","key":"884_CR230","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1148\/radiol.2323032035","volume":"232","author":"SG Armato","year":"2004","unstructured":"Armato, S.G., et al.: Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232(3), 739\u2013748 (2004). https:\/\/doi.org\/10.1148\/radiol.2323032035","journal-title":"Radiology"},{"issue":"3","key":"884_CR231","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.compmedimag.2011.07.003","volume":"36","author":"A Depeursinge","year":"2012","unstructured":"Depeursinge, A., Vargas, A., Platon, A., Geissbuhler, A., Poletti, P.-A., M\u00fcller, H.: Building a reference multimedia database for interstitial lung diseases. Comput. Med. Imaging Graph. 36(3), 227\u2013238 (2012). https:\/\/doi.org\/10.1016\/j.compmedimag.2011.07.003","journal-title":"Comput. Med. Imaging Graph."},{"issue":"4","key":"884_CR232","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abr\u00e0moff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004). https:\/\/doi.org\/10.1109\/TMI.2004.825627","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"884_CR233","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/42.845178","volume":"19","author":"AD Hoover","year":"2000","unstructured":"Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203\u2013210 (2000). https:\/\/doi.org\/10.1109\/42.845178","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"884_CR234","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","volume":"SMC-3","author":"RM Haralick","year":"1973","unstructured":"Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man. Cybern. SMC-3(6), 610\u2013621 (1973). https:\/\/doi.org\/10.1109\/TSMC.1973.4309314","journal-title":"IEEE Trans. Syst. Man. Cybern."},{"key":"884_CR235","doi-asserted-by":"publisher","unstructured":"\u00c7amlica, Z., Tizhoosh, H. R. and Khalvati, F.: Medical image classification via SVM using LBP features from saliency-based folded data. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). pp. 128\u2013132 (2015). https:\/\/doi.org\/10.1109\/ICMLA.2015.131","DOI":"10.1109\/ICMLA.2015.131"},{"key":"884_CR236","doi-asserted-by":"publisher","first-page":"58006","DOI":"10.1109\/ACCESS.2020.2981337","volume":"8","author":"RJS Raj","year":"2020","unstructured":"Raj, R.J.S., Shobana, S.J., Pustokhina, I.V., Pustokhin, D.A., Gupta, D., Shankar, K.: Optimal feature selection-based medical image classification using deep learning model in internet of medical things. IEEE Access 8, 58006\u201358017 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2981337","journal-title":"IEEE Access"},{"key":"884_CR237","doi-asserted-by":"crossref","unstructured":"Miller Jr, R. G.: Beyond ANOVA: basics of applied statistics. CRC press (1997)","DOI":"10.1201\/b15236"},{"key":"884_CR238","unstructured":"Surendiran, A., Vadivel, B.: Feature selection using stepwise ANOVA discriminant analysis for mammogram mass classification. Int. J. Signal Image Process. 2(1), 17 (2011). https:\/\/www.researchgate.net\/profile\/Surendiran_Balasubramanian\/publication\/258052973_Feature_selection_using_stepwise_ANOVA_discriminant_analysis_for_mammogram_mass_classification\/links\/0c96052942c3e97cda000000.pdf"},{"key":"884_CR239","doi-asserted-by":"crossref","unstructured":"Theodoridis, D.: Sergios and Pikrakis, Aggelos and Koutroumbas, Konstantinos and Cavouras, Introduction to pattern recognition: a matlab approach. 2010","DOI":"10.1016\/B978-1-59749-272-0.50003-7"},{"key":"884_CR240","first-page":"1363","volume":"2016","author":"A Wu","year":"2016","unstructured":"Wu, A., Xu, Z., Gao, M., Mollura, D.J.: Deep vessel tracking: a generalized probabilistic approach via deep learning Aaron Wu, Ziyue Xu?, Mingchen Gao, Mario Buty, Daniel J. Mollura Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, MD 20892. Isbi 2016, 1363\u20131367 (2016)","journal-title":"Isbi"},{"key":"884_CR241","first-page":"979","volume":"2017","author":"Z Hussain","year":"2017","unstructured":"Hussain, Z., Gimenez, F., Yi, D., Rubin, D.: Differential data augmentation techniques for medical imaging classification tasks. AMIA \u2026 Annu. Symp. Proc. AMIA Symp. 2017, 979\u2013984 (2017)","journal-title":"AMIA \u2026 Annu. Symp. Proc. AMIA Symp."},{"issue":"2","key":"884_CR242","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","volume":"11","author":"KH Zou","year":"2004","unstructured":"Zou, K.H., et al.: Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad. Radiol. 11(2), 178\u2013189 (2004). https:\/\/doi.org\/10.1016\/S1076-6332(03)00671-8","journal-title":"Acad. Radiol."},{"key":"884_CR243","unstructured":"Cho, J., Lee, K., Shin, E., Choy, G. and Do, S.: Medical image deep learning with hospital {PACS} dataset. CoRR vol. abs\/1511.0, (2015). Available: http:\/\/arxiv.org\/abs\/1511.06348"},{"key":"884_CR244","unstructured":"Guibas, J. T., Virdi, T. S. and Li, P. S.: Synthetic medical images from dual generative adversarial networks. CoRR vol. abs\/1709.0, (2017). Available: http:\/\/arxiv.org\/abs\/1709.01872"},{"key":"884_CR245","doi-asserted-by":"crossref","unstructured":"Moeskops, P., Veta, M., Lafarge, M. W., Eppenhof, K. A. J. and Pluim, J. P. W.: Adversarial training and dilated convolutions for brain MRI segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. pp. 56\u201364 (2017)","DOI":"10.1007\/978-3-319-67558-9_7"},{"issue":"2","key":"884_CR246","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neunet.2007.12.031","volume":"21","author":"MA Mazurowski","year":"2008","unstructured":"Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw. 21(2), 427\u2013436 (2008). https:\/\/doi.org\/10.1016\/j.neunet.2007.12.031","journal-title":"Neural Netw."},{"key":"884_CR247","doi-asserted-by":"publisher","unstructured":"Galar, M., Fernandez, A., Barrenechea, E., Bustince, H. and Herrera, F.: A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(4), 463\u2013484 (2012). https:\/\/doi.org\/10.1109\/TSMCC.2011.2161285","DOI":"10.1109\/TSMCC.2011.2161285"},{"key":"884_CR248","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.media.2016.06.032","volume":"33","author":"M de Bruijne","year":"2016","unstructured":"de Bruijne, M.: Machine learning approaches in medical image analysis: from detection to diagnosis. Med. Image Anal. 33, 94\u201397 (2016). https:\/\/doi.org\/10.1016\/j.media.2016.06.032","journal-title":"Med. Image Anal."},{"issue":"2","key":"884_CR249","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.zemedi.2018.11.002","volume":"29","author":"AS Lundervold","year":"2019","unstructured":"Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Z. Med. Phys. 29(2), 102\u2013127 (2019). https:\/\/doi.org\/10.1016\/j.zemedi.2018.11.002","journal-title":"Z. Med. Phys."},{"key":"884_CR250","doi-asserted-by":"crossref","unstructured":"Nie, D., Cao, X., Gao, Y., Wang, L. and Shen, D.: Estimating CT image from MRI data using 3D fully convolutional networks. In: Deep Learning and Data Labeling for Medical Applications, pp. 170\u2013178 (2016)","DOI":"10.1007\/978-3-319-46976-8_18"},{"key":"884_CR251","doi-asserted-by":"crossref","unstructured":"Ledig, C., et al.: \u201c\\href{https:\/\/ieeexplore.ieee.org\/abstract\/document\/8099502}{Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network},\u201d Cvpr, vol. 2, no. 3, p. 4, 2017. Available: http:\/\/openaccess.thecvf.com\/content_cvpr_2017\/papers\/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf","DOI":"10.1109\/CVPR.2017.19"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00884-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-021-00884-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-021-00884-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T11:03:05Z","timestamp":1744196585000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-021-00884-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,21]]},"references-count":251,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["884"],"URL":"https:\/\/doi.org\/10.1007\/s00530-021-00884-5","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,21]]},"assertion":[{"value":"9 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Conflict of interest on the behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}