{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:14:55Z","timestamp":1743034495749,"version":"3.40.3"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031783883"},{"type":"electronic","value":"9783031783890"}],"license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78389-0_8","type":"book-chapter","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T14:14:22Z","timestamp":1733321662000},"page":"110-125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cystic Adenocarcinoma Segmentation Based on Multi-frequency and Multi-scale SimAM Attention"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5327-8451","authenticated-orcid":false,"given":"Xia","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6917-1404","authenticated-orcid":false,"given":"Jian","family":"Tan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5762-5763","authenticated-orcid":false,"given":"Bailing","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-6485","authenticated-orcid":false,"given":"Guodong","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8842-6388","authenticated-orcid":false,"given":"Zeyang","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6027-1091","authenticated-orcid":false,"given":"Rui","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3986-4266","authenticated-orcid":false,"given":"Jing Qiu","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7038-3789","authenticated-orcid":false,"given":"Chaoyi","family":"Pang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105209","volume":"142","author":"S Prabhu","year":"2022","unstructured":"Prabhu, S., Prasad, K., Robles-Kelly, A., Lu, X.: AI-based carcinoma detection and classification using histopathological images: A systematic review. Comput. Biol. Medicine. 142, 105209 (2022)","journal-title":"Comput. Biol. Medicine."},{"key":"8_CR2","doi-asserted-by":"publisher","first-page":"78075","DOI":"10.1109\/ACCESS.2019.2920980","volume":"7","author":"P Monkam","year":"2019","unstructured":"Monkam, P., Qi, S., Ma, H., Gao, W., Yao, Y.-D., Qian, W.: Detection and Classification of Pulmonary Nodules Using Convolutional Neural Networks: A Survey. IEEE Access. 7, 78075\u201378091 (2019)","journal-title":"IEEE Access."},{"issue":"1","key":"8_CR3","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s11277-020-07732-1","volume":"116","author":"A Naik","year":"2021","unstructured":"Naik, A., Edla, D.R.: Lung Nodule Classification on Computed Tomography Images Using Deep Learning. Wirel. Pers. Commun. 116(1), 655\u2013690 (2021)","journal-title":"Wirel. Pers. Commun."},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.media.2019.03.010","volume":"55","author":"M Winkels","year":"2019","unstructured":"Winkels, M., Cohen, T.S.: Pulmonary nodule detection in CT scans with equivariant CNNs. Medical Image Anal. 55, 15\u201326 (2019)","journal-title":"Medical Image Anal."},{"key":"8_CR5","doi-asserted-by":"publisher","first-page":"154007","DOI":"10.1109\/ACCESS.2020.3018666","volume":"8","author":"W Cao","year":"2020","unstructured":"Cao, W., Wu, R., Cao, G., He, Z.: A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans. IEEE Access. 8, 154007\u2013154023 (2020)","journal-title":"IEEE Access."},{"issue":"2","key":"8_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-020-9050-z","volume":"15","author":"WJ Sori","year":"2021","unstructured":"Sori, W.J., Jiang, F., Godana, A.W., Liu, S., Jobir, G.D.: DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Frontiers Comput. Sci. 15(2), 152701 (2021)","journal-title":"Frontiers Comput. Sci."},{"issue":"1","key":"8_CR7","doi-asserted-by":"publisher","first-page":"771","DOI":"10.2991\/ijcis.d.200608.001","volume":"13","author":"S Pang","year":"2020","unstructured":"Pang, S., Meng, F., Wang, X., Wang, J., Song, T., Wang, X., Cheng, X.: VGG16-T: A Novel Deep Convolutional Neural Network with Boosting to Identify Pathological Type of Lung Cancer in Early Stage by CT Images. Int. J. Comput. Intell. Syst. 13(1), 771\u2013780 (2020)","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"8_CR8","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.neucom.2020.06.144","volume":"453","author":"MMN Abid","year":"2021","unstructured":"Abid, M.M.N., Zia, T., Ghafoor, M., Windridge, D.: Multi-view Convolutional Recurrent Neural Networks for Lung Cancer Nodule Identification. Neurocomputing 453, 299\u2013311 (2021)","journal-title":"Neurocomputing"},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.compbiomed.2018.10.033","volume":"103","author":"G Zhang","year":"2018","unstructured":"Zhang, G., Jiang, S., Yang, Z., Gong, L., Ma, X., Zhou, Z., Bao, C., Liu, Q.: Automatic nodule detection for lung cancer in CT images: A review. Comput. Biol. Medicine. 103, 287\u2013300 (2018)","journal-title":"Comput. Biol. Medicine."},{"issue":"3","key":"8_CR10","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1007\/s10278-020-00320-6","volume":"33","author":"A Halder","year":"2020","unstructured":"Halder, A., Dey, D., Sadhu, A.K.: Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review. J. Digit. Imaging 33(3), 655\u2013677 (2020)","journal-title":"J. Digit. Imaging"},{"key":"8_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, G., Yang, Z., Gong, L., Jiang, S., Wang, L., Cao, X., Wei, L., Zhang, H., Liu, Z. An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images. J. Medical Syst. 43(7), 181:1\u2013181:18 (2019)","DOI":"10.1007\/s10916-019-1327-0"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Thakur, S.K., Singh, D.P., Choudhary, J. Lung cancer identification: a review on detection and classification. Cancer and Metastasis Reviews. 39(3), 989\u2013998 (2020). Springer","DOI":"10.1007\/s10555-020-09901-x"},{"key":"8_CR13","doi-asserted-by":"crossref","unstructured":"Detterbeck, F.C., Mazzone, P.J., Naidich, D.P., Bach, P.B. Screening for lung cancer: diagnosis and management of lung cancer: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 143(5), e78S\u2013e92S (2013). Elsevier","DOI":"10.1378\/chest.12-2350"},{"key":"8_CR14","doi-asserted-by":"crossref","unstructured":"Adiraju, R.V., Elias, S. A survey on lung CT datasets and research trends. Research on Biomedical Engineering. 37(2), 403\u2013418 (2021). Springer","DOI":"10.1007\/s42600-021-00138-3"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Yu, H., Li, J., Zhang, L., Cao, Y., Yu, X., Sun, J. Design of lung nodules segmentation and recognition algorithm based on deep learning. BMC bioinformatics. 22, 1\u201321 (2021). Springer","DOI":"10.1186\/s12859-021-04234-0"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Alakwaa, W., Nassef, M., Badr, A. Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). International Journal of Advanced Computer Science and Applications. 8(8) (2017). Science and Information (SAI) Organization Limited","DOI":"10.14569\/IJACSA.2017.080853"},{"issue":"2","key":"8_CR17","doi-asserted-by":"publisher","first-page":"114","DOI":"10.5603\/ARM.2019.0018","volume":"87","author":"L Opoka","year":"2019","unstructured":"Opoka, L., Szturmowicz, M., Oniszh, K., Korzybski, D., Podgajny, Z., Blasi\u0144ska-Przerwa, K., Szo\u0142kowska, M., Bestry, I.: CT imaging features of thin-walled cavitary squamous cell lung cancer. Advances in Respiratory Medicine. 87(2), 114\u2013117 (2019)","journal-title":"Advances in Respiratory Medicine."},{"key":"8_CR18","unstructured":"Womack, N.A., Graham, E.A. Epithelial metaplasia in congenital cystic disease of the lung: Its possible relation to carcinoma of the bronchus. The American Journal of Pathology. (5), 645 (1941). American Society for Investigative Pathology"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Woodring, J.H., Fried, A.M., Chuang, V.P. Solitary cavities of the lung: diagnostic implications of cavity wall thickness. American Journal of Roentgenology. 135(6), 1269\u20131271 (1980). Am Roentgen Ray Soc","DOI":"10.2214\/ajr.135.6.1269"},{"key":"8_CR20","unstructured":"Yang, L., Zhang, R.-Y., Li, L., Xie, X. SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event. Proceedings of Machine Learning Research, vol. 139, pp. 11863\u201311874. PMLR (2021)"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Nam, J.-H., Syazwany, N.S., Kim, S.J., Lee, S.-C. Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11480\u201311491 (2024)","DOI":"10.1109\/CVPR52733.2024.01091"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells III, W.M., Frangi, A.F. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference Munich, Germany, October 5 - 9, 2015, Proceedings, Part III. Lecture Notes in Computer Science, vol. 9351, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8_CR23","unstructured":"Zhao, X., Jia, H., Pang, Y., Lv, L., Tian, F., Zhang, L., Sun, W., Lu, H. M$$^2$$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation. CoRR. abs\/2303.10894 (2023)"},{"key":"8_CR24","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J., Oktay, O., Schaap, M., Heinrich, M.P., Kainz, B., Glocker, B., Rueckert, D.: Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Anal. 53, 197\u2013207 (2019)","journal-title":"Medical Image Anal."},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Gao, Y., Huang, R., Chen, M., Wang, Z., Deng, J., Chen, Y., Yang, Y., Zhang, J., Tao, C., Li, H. FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images. CoRR. abs\/1907.12056 (2019)","DOI":"10.1007\/978-3-030-32248-9_92"},{"key":"8_CR26","unstructured":"Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M.C.H., Heinrich, M.P., Misawa, K., Mori, K., McDonagh, S.G., Hammerla, N.Y., Kainz, B., Glocker, B., Rueckert, D. Attention U-Net: Learning Where to Look for the Pancreas. CoRR. abs\/1804.03999 (2018)"},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., Huang, Q. Cascaded Partial Decoder for Fast and Accurate Salient Object Detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 3907\u20133916. Computer Vision Foundation \/ IEEE (2019)","DOI":"10.1109\/CVPR.2019.00403"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods. 18(2), 203\u2013211 (2021). Nature Publishing Group","DOI":"10.1038\/s41592-020-01008-z"},{"key":"8_CR29","doi-asserted-by":"crossref","unstructured":"Webb, B.S., Dhruv, N.T., Solomon, S.G., Tailby, C., Lennie, P. Early and late mechanisms of surround suppression in striate cortex of macaque. Journal of Neuroscience. 25(50), 11666\u201311675 (2005). Soc Neuroscience","DOI":"10.1523\/JNEUROSCI.3414-05.2005"},{"key":"8_CR30","doi-asserted-by":"crossref","unstructured":"Hettihewa, K., Kobchaisawat, T., Tanpowpong, N., Chalidabhongse, T.H. MANet: a multi-attention network for automatic liver tumor segmentation in computed tomography (CT) imaging. Scientific Reports. 13(1), 20098 (2023). Nature Publishing Group UK London","DOI":"10.1038\/s41598-023-46580-4"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, J., Ni, J., Elazab, A., Wu, J. TA-Net: Triple attention network for medical image segmentation. Computers in Biology and Medicine. 137, 104836 (2021). Elsevier","DOI":"10.1016\/j.compbiomed.2021.104836"},{"key":"8_CR32","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. CoRR. abs\/1511.00561 (2015)"},{"key":"8_CR33","unstructured":"Gao, S., Cheng, M.-M., Zhao, K., Zhang, X., Yang, M.-H., Torr, P.H.S. Res2Net: A New Multi-scale Backbone Architecture. CoRR. abs\/1904.01169 (2019)"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J. Deep Residual Learning for Image Recognition. CoRR. abs\/1512.03385 (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"8_CR35","unstructured":"Zhang, H., Wu, C., Zhang, Z., Zhu, Y., Zhang, Z., Lin, H., Sun, Y., He, T., Mueller, J., Manmatha, R., Li, M., Smola, A.J. ResNeSt: Split-Attention Networks. CoRR. abs\/2004.08955 (2020)"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G. Squeeze-and-Excitation Networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 7132\u20137141. Computer Vision Foundation \/ IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"8_CR37","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R.B., Gupta, A., He, K. Non-Local Neural Networks. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, pp. 7794\u20137803. Computer Vision Foundation \/ IEEE Computer Society (2018)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"8_CR38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S. CBAM: Convolutional Block Attention Module. CoRR. abs\/1807.06521 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"Sagar, A. DMSANet: Dual Multi Scale Attention Network. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds.) Image Analysis and Processing - ICIAP 2022 - 21st International Conference, Lecce, Italy, May 23-27, 2022, Proceedings, Part I. Lecture Notes in Computer Science, vol. 13231, pp. 633\u2013645. Springer (2022)","DOI":"10.1007\/978-3-031-06427-2_53"},{"key":"8_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Q Xu","year":"2023","unstructured":"Xu, Q., Ma, Z., He, N., Duan, W.: DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation. Comput. Biol. Medicine. 154, 106626 (2023)","journal-title":"Comput. Biol. Medicine."},{"key":"8_CR41","doi-asserted-by":"crossref","unstructured":"Liu, S., Huang, D., Wang, Y. Receptive Field Block Net for Accurate and Fast Object Detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision - ECCV 2018 - 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XI. Lecture Notes in Computer Science, vol. 11215, pp. 404\u2013419. Springer (2018)","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"8_CR42","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V. EfficientDet: Scalable and Efficient Object Detection. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pp. 10778\u201310787. Computer Vision Foundation \/ IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"8_CR43","doi-asserted-by":"crossref","unstructured":"Su, R., Zhang, D., Liu, J., Cheng, C. MSU-Net: Multi-scale U-Net for 2D medical image segmentation. Frontiers in Genetics. 12, 639930 (2021). Frontiers Media SA","DOI":"10.3389\/fgene.2021.639930"},{"key":"8_CR44","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S. Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"issue":"1","key":"8_CR45","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/T-C.1974.223784","volume":"100","author":"N Ahmed","year":"1974","unstructured":"Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transform. IEEE Trans. Comput. 100(1), 90\u201393 (1974)","journal-title":"IEEE Trans. Comput."},{"issue":"10","key":"8_CR46","doi-asserted-by":"publisher","first-page":"2464","DOI":"10.1109\/78.157290","volume":"40","author":"MJ Shensa","year":"1992","unstructured":"Shensa, M.J.: The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans. Signal Process. 40(10), 2464\u20132482 (1992)","journal-title":"IEEE Trans. Signal Process."},{"key":"8_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104815","volume":"137","author":"M Yeung","year":"2021","unstructured":"Yeung, M., Sala, E., Sch\u00f6nlieb, C.-B., Rundo, L.: Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy. Comput. Biol. Medicine. 137, 104815 (2021)","journal-title":"Comput. Biol. Medicine."},{"issue":"11","key":"8_CR48","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)","journal-title":"Proc. IEEE"},{"key":"8_CR49","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I. Attention is All you Need. In: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pp. 5998\u20136008 (2017)"},{"key":"8_CR50","doi-asserted-by":"crossref","unstructured":"Qin, Z., Zhang, P., Wu, F., Li, X. FcaNet: Frequency Channel Attention Networks. CoRR. abs\/2012.11879 (2020)","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"8_CR51","doi-asserted-by":"crossref","unstructured":"Changhez, J., James, S., Jamala, F., Khan, S., Khan, M.Z., Gul, S., Zainab, I. Evaluating the Efficacy and Accuracy of AI-Assisted Diagnostic Techniques in Endometrial Carcinoma: A Systematic Review. Cureus. 16(5) (2024). Cureus","DOI":"10.7759\/cureus.60973"},{"key":"8_CR52","doi-asserted-by":"crossref","unstructured":"Hu, R., Li, H., Horng, H., Thomasian, N.M., Jiao, Z., Zhu, C., Zou, B., Bai, H.X. Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI. Scientific reports. 12(1), 7924 (2022). Nature Publishing Group UK London","DOI":"10.1038\/s41598-022-11997-w"},{"key":"8_CR53","doi-asserted-by":"crossref","unstructured":"Abdelrahim, M., Saiko, M., Maeda, N., Hossain, E., Alkandari, A., Subramaniam, S., Parra-Blanco, A., Sanchez-Yague, A., Coron, E., Repici, A. Development and validation of artificial neural networks model for detection of Barrett\u2019s neoplasia: a multicenter pragmatic nonrandomized trial (with video). Gastrointestinal Endoscopy. 97(3), 422\u2013434 (2023). Elsevier","DOI":"10.1016\/j.gie.2022.10.031"},{"key":"8_CR54","doi-asserted-by":"crossref","unstructured":"Cao, K., Xia, Y., Yao, J., Han, X., Lambert, L., Zhang, T., Tang, W., Jin, G., Jiang, H., Fang, X. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nature medicine. 29(12), 3033\u20133043 (2023). Nature Publishing Group US New York","DOI":"10.1038\/s41591-023-02640-w"},{"key":"8_CR55","doi-asserted-by":"crossref","unstructured":"Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L. PraNet: Parallel Reverse Attention Network for Polyp Segmentation. CoRR.abs\/2006.11392 (2020)","DOI":"10.1007\/978-3-030-59725-2_26"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78389-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T15:05:30Z","timestamp":1733324730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78389-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"ISBN":["9783031783883","9783031783890"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78389-0_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,5]]},"assertion":[{"value":"5 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}