{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T22:31:12Z","timestamp":1781130672524,"version":"3.54.1"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031120527","type":"print"},{"value":"9783031120534","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-12053-4_44","type":"book-chapter","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T09:15:50Z","timestamp":1658740550000},"page":"594-608","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Jointly Boosting Saliency Prediction and\u00a0Disease Classification on\u00a0Chest X-ray Images with\u00a0Multi-task UNet"],"prefix":"10.1007","author":[{"given":"Hongzhi","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Rohling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Septimiu","family":"Salcudean","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"44_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104037","volume":"126","author":"A Amyar","year":"2020","unstructured":"Amyar, A., Modzelewski, R., Li, H., Ruan, S.: Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: classification and segmentation. Comput. Biol. Med. 126, 104037 (2020)","journal-title":"Comput. Biol. Med."},{"key":"44_CR2","doi-asserted-by":"crossref","unstructured":"Borji, A.: Saliency prediction in the deep learning era: successes and limitations. IEEE Trans. Patt. Anal. Mach. Intell. 43, 679\u2013700 (2019)","DOI":"10.1109\/TPAMI.2019.2935715"},{"issue":"1","key":"44_CR3","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TIP.2012.2210727","volume":"22","author":"A Borji","year":"2012","unstructured":"Borji, A., Sihite, D.N., Itti, L.: Quantitative analysis of human-model agreement in visual saliency modeling: a comparative study. IEEE Trans. Image Process. 22(1), 55\u201369 (2012)","journal-title":"IEEE Trans. Image Process."},{"issue":"3","key":"44_CR4","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1109\/TPAMI.2018.2815601","volume":"41","author":"Z Bylinskii","year":"2018","unstructured":"Bylinskii, Z., Judd, T., Oliva, A., Torralba, A., Durand, F.: What do different evaluation metrics tell us about saliency models? IEEE Trans. Pattern Anal. Mach. Intell. 41(3), 740\u2013757 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"44_CR5","doi-asserted-by":"publisher","unstructured":"Cai, Y., Sharma, H., Chatelain, P., Noble, J.A.: Multi-task SonoEyeNet: detection of fetal standardized planes assisted by generated sonographer attention maps. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 871\u2013879. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_98","DOI":"10.1007\/978-3-030-00928-1_98"},{"key":"44_CR6","doi-asserted-by":"crossref","unstructured":"\u00c7all\u0131, E., Sogancioglu, E., van Ginneken, B., van Leeuwen, K.G., Murphy, K.: Deep learning for chest x-ray analysis: a survey. Med. Image Anal. 72, 102125 (2021)","DOI":"10.1016\/j.media.2021.102125"},{"key":"44_CR7","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-030-60796-8_8","volume-title":"Intelligent Computing Methodologies","author":"G Cao","year":"2020","unstructured":"Cao, G., Tang, Q., Jo, K.: Aggregated deep saliency prediction by self-attention network. In: Huang, D.-S., Premaratne, P. (eds.) ICIC 2020. LNCS (LNAI), vol. 12465, pp. 87\u201397. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60796-8_8"},{"issue":"1","key":"44_CR8","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41\u201375 (1997)","journal-title":"Mach. Learn."},{"issue":"1","key":"44_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-020-17478-w","volume":"11","author":"DC Castro","year":"2020","unstructured":"Castro, D.C., Walker, I., Glocker, B.: Causality matters in medical imaging. Nat. Commun. 11(1), 1\u201310 (2020)","journal-title":"Nat. Commun."},{"key":"44_CR10","unstructured":"Chen, Z., Badrinarayanan, V., Lee, C.Y., Rabinovich, A.: GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. In: International Conference on Machine Learning, pp. 794\u2013803. PMLR (2018)"},{"key":"44_CR11","unstructured":"Crawshaw, M.: Multi-task learning with deep neural networks: a survey. arXiv preprint arXiv:2009.09796 (2020)"},{"key":"44_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/978-3-540-74690-4_26","volume-title":"Artificial Neural Networks \u2013 ICANN 2007","author":"S Duffner","year":"2007","unstructured":"Duffner, S., Garcia, C.: An online backpropagation algorithm with validation error-based adaptive learning rate. In: de S\u00e1, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 249\u2013258. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-74690-4_26"},{"key":"44_CR13","series-title":"Studies in Big Data","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/978-3-030-74575-2_14","volume-title":"Artificial Intelligence and Blockchain for Future Cybersecurity Applications","author":"K El Asnaoui","year":"2021","unstructured":"El Asnaoui, K., Chawki, Y., Idri, A.: Automated methods for detection and classification pneumonia based on X-Ray images using deep learning. In: Maleh, Y., Baddi, Y., Alazab, M., Tawalbeh, L., Romdhani, I. (eds.) Artificial Intelligence and Blockchain for Future Cybersecurity Applications. SBD, vol. 90, pp. 257\u2013284. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-74575-2_14"},{"issue":"8","key":"44_CR14","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"issue":"5","key":"44_CR15","doi-asserted-by":"publisher","first-page":"544","DOI":"10.3390\/rs11050544","volume":"11","author":"K Fu","year":"2019","unstructured":"Fu, K., Dai, W., Zhang, Y., Wang, Z., Yan, M., Sun, X.: MultiCAM: multiple class activation mapping for aircraft recognition in remote sensing images. Remote Sens. 11(5), 544 (2019)","journal-title":"Remote Sens."},{"key":"44_CR16","doi-asserted-by":"crossref","unstructured":"Guo, M., Haque, A., Huang, D.A., Yeung, S., Fei-Fei, L.: Dynamic task prioritization for multitask learning. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 270\u2013287 (2018)","DOI":"10.1007\/978-3-030-01270-0_17"},{"issue":"2","key":"44_CR17","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1023\/A:1010920819831","volume":"45","author":"DJ Hand","year":"2001","unstructured":"Hand, D.J., Till, R.J.: A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach. Learn. 45(2), 171\u2013186 (2001)","journal-title":"Mach. Learn."},{"key":"44_CR18","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"44_CR19","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"44_CR20","doi-asserted-by":"crossref","unstructured":"Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 590\u2013597 (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"44_CR21","doi-asserted-by":"crossref","unstructured":"Jha, A., Kumar, A., Pande, S., Banerjee, B., Chaudhuri, S.: MT-UNET: a novel U-Net based multi-task architecture for visual scene understanding. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 2191\u20132195. IEEE (2020)","DOI":"10.1109\/ICIP40778.2020.9190695"},{"key":"44_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2020.103887","volume":"95","author":"S Jia","year":"2020","unstructured":"Jia, S., Bruce, N.D.: EML-NET: an expandable multi-layer network for saliency prediction. Image Vis. Comput. 95, 103887 (2020)","journal-title":"Image Vis. Comput."},{"key":"44_CR23","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci. Data 6(1), 1\u20138 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"44_CR24","doi-asserted-by":"crossref","unstructured":"Johnson, A.E., et al.: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv preprint arXiv:1901.07042 (2019)","DOI":"10.1038\/s41597-019-0322-0"},{"key":"44_CR25","doi-asserted-by":"crossref","unstructured":"Karargyris, A., et al.: Creation and validation of a chest x-ray dataset with eye-tracking and report dictation for AI development. Sci. Data 8(1), 1\u201318 (2021)","DOI":"10.1038\/s41597-021-00863-5"},{"key":"44_CR26","doi-asserted-by":"crossref","unstructured":"Karessli, N., Akata, Z., Schiele, B., Bulling, A.: Gaze embeddings for zero-shot image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4525\u20134534 (2017)","DOI":"10.1109\/CVPR.2017.679"},{"key":"44_CR27","unstructured":"Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7482\u20137491 (2018)"},{"key":"44_CR28","doi-asserted-by":"crossref","unstructured":"Khan, W., Zaki, N., Ali, L.: Intelligent pneumonia identification from chest x-rays: a systematic literature review. IEEE Access 9, 51747\u201351771 (2021)","DOI":"10.1109\/ACCESS.2021.3069937"},{"key":"44_CR29","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"44_CR30","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.neunet.2020.05.004","volume":"129","author":"A Kroner","year":"2020","unstructured":"Kroner, A., Senden, M., Driessens, K., Goebel, R.: Contextual encoder-decoder network for visual saliency prediction. Neural Netw. 129, 261\u2013270 (2020)","journal-title":"Neural Netw."},{"key":"44_CR31","doi-asserted-by":"crossref","unstructured":"K\u00fcmmerer, M., Wallis, T.S., Bethge, M.: DeepGaze II: reading fixations from deep features trained on object recognition. arXiv preprint arXiv:1610.01563 (2016)","DOI":"10.1167\/17.10.1147"},{"key":"44_CR32","doi-asserted-by":"crossref","unstructured":"Li, H., Li, J., Guan, X., Liang, B., Lai, Y., Luo, X.: Research on overfitting of deep learning. In: 2019 15th International Conference on Computational Intelligence and Security (CIS), pp. 78\u201381. IEEE (2019)","DOI":"10.1109\/CIS.2019.00025"},{"key":"44_CR33","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, Z., Dai, C., Dong, Q., Badrigilan, S.: Accuracy of deep learning for automated detection of pneumonia using chest x-ray images: a systematic review and meta-analysis. Comput. Biol. Med. 123, 103898 (2020)","DOI":"10.1016\/j.compbiomed.2020.103898"},{"key":"44_CR34","unstructured":"Liebel, L., K\u00f6rner, M.: Auxiliary tasks in multi-task learning. arXiv preprint arXiv:1805.06334 (2018)"},{"key":"44_CR35","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":"3","key":"44_CR36","first-page":"154","volume":"4","author":"X Liu","year":"2018","unstructured":"Liu, X., Milanova, M.: Visual attention in deep learning: a review. Int. Rob. Auto J. 4(3), 154\u2013155 (2018)","journal-title":"Int. Rob. Auto J."},{"key":"44_CR37","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)"},{"key":"44_CR38","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.ijmedinf.2017.03.001","volume":"105","author":"L McLaughlin","year":"2017","unstructured":"McLaughlin, L., Bond, R., Hughes, C., McConnell, J., McFadden, S.: Computing eye gaze metrics for the automatic assessment of radiographer performance during x-ray image interpretation. Int. J. Med. Inform. 105, 11\u201321 (2017)","journal-title":"Int. J. Med. Inform."},{"key":"44_CR39","doi-asserted-by":"crossref","unstructured":"Moody, G., Mark, R., Goldberger, A.: PhysioNet: a research resource for studies of complex physiologic and biomedical signals. In: Computers in Cardiology 2000, vol. 27 (Cat. 00CH37163), pp. 179\u2013182. IEEE (2000)","DOI":"10.1109\/CIC.2000.898485"},{"key":"44_CR40","doi-asserted-by":"crossref","unstructured":"Moradi, S., et al.: MFP-Unet: a novel deep learning based approach for left ventricle segmentation in echocardiography. Physica Med. 67, 58\u201369 (2019)","DOI":"10.1016\/j.ejmp.2019.10.001"},{"key":"44_CR41","doi-asserted-by":"crossref","unstructured":"Oyama, T., Yamanaka, T.: Fully convolutional DenseNet for saliency-map prediction. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 334\u2013339. IEEE (2017)","DOI":"10.1109\/ACPR.2017.143"},{"issue":"3","key":"44_CR42","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1049\/trit.2018.1012","volume":"3","author":"T Oyama","year":"2018","unstructured":"Oyama, T., Yamanaka, T.: Influence of image classification accuracy on saliency map estimation. CAAI Trans. Intell. Technol. 3(3), 140\u2013152 (2018)","journal-title":"CAAI Trans. Intell. Technol."},{"key":"44_CR43","doi-asserted-by":"crossref","unstructured":"Pan, J., Sayrol, E., Giro-i Nieto, X., McGuinness, K., O\u2019Connor, N.E.: Shallow and deep convolutional networks for saliency prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 598\u2013606 (2016)","DOI":"10.1109\/CVPR.2016.71"},{"key":"44_CR44","doi-asserted-by":"crossref","unstructured":"Paneri, S., Gregoriou, G.G.: Top-down control of visual attention by the prefrontal cortex. functional specialization and long-range interactions. Front. Neurosci. 11, 545 (2017)","DOI":"10.3389\/fnins.2017.00545"},{"key":"44_CR45","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. Adv. Neural. Inf. Process. Syst. 32, 8026\u20138037 (2019)"},{"key":"44_CR46","doi-asserted-by":"crossref","unstructured":"Reddy, N., Jain, S., Yarlagadda, P., Gandhi, V.: Tidying deep saliency prediction architectures. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10241\u201310247. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341574"},{"key":"44_CR47","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"44_CR48","unstructured":"Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-cam: why did you say that? arXiv preprint arXiv:1611.07450 (2016)"},{"key":"44_CR49","unstructured":"Sener, O., Koltun, V.: Multi-task learning as multi-objective optimization. arXiv preprint arXiv:1810.04650 (2018)"},{"key":"44_CR50","doi-asserted-by":"crossref","unstructured":"Serte, S., Serener, A., Al-Turjman, F.: Deep learning in medical imaging: a brief review. Trans. Emerg. Telecommun. Technol. 14 (2020)","DOI":"10.1002\/ett.4080"},{"key":"44_CR51","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)"},{"key":"44_CR52","unstructured":"Smith, L.N.: A disciplined approach to neural network hyper-parameters: part 1-learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:1803.09820 (2018)"},{"issue":"1","key":"44_CR53","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s00530-021-00796-4","volume":"28","author":"Y Sun","year":"2021","unstructured":"Sun, Y., Zhao, M., Hu, K., Fan, S.: Visual saliency prediction using multi-scale attention gated network. Multimedia Syst. 28(1), 131\u2013139 (2021). https:\/\/doi.org\/10.1007\/s00530-021-00796-4","journal-title":"Multimedia Syst."},{"key":"44_CR54","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"44_CR55","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"44_CR56","unstructured":"Tan, M., Le, Q.V.: Efficientnetv2: smaller models and faster training. arXiv preprint arXiv:2104.00298 (2021)"},{"key":"44_CR57","unstructured":"Tieleman, T., Hinton, G., et al.: Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural Netw. Mach. Learn. 4(2), 26\u201331 (2012)"},{"key":"44_CR58","doi-asserted-by":"crossref","unstructured":"Vandenhende, S., Georgoulis, S., Van Gansbeke, W., Proesmans, M., Dai, D., Van Gool, L.: Multi-task learning for dense prediction tasks: a survey. IEEE Trans. Patt. Anal. Mach. Intell. 44(7) (2021)","DOI":"10.1109\/TPAMI.2021.3054719"},{"key":"44_CR59","doi-asserted-by":"crossref","unstructured":"Wang, W., Tran, D., Feiszli, M.: What makes training multi-modal classification networks hard? In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12695\u201312705 (2020)","DOI":"10.1109\/CVPR42600.2020.01271"},{"issue":"1","key":"44_CR60","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TPAMI.2019.2924417","volume":"43","author":"W Wang","year":"2019","unstructured":"Wang, W., Shen, J., Xie, J., Cheng, M.M., Ling, H., Borji, A.: Revisiting video saliency prediction in the deep learning era. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 220\u2013237 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"44_CR61","doi-asserted-by":"crossref","unstructured":"Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: ChestX-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2097\u20132106 (2017)","DOI":"10.1109\/CVPR.2017.369"},{"key":"44_CR62","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Yang, Q.: A survey on multi-task learning. In: IEEE Transactions on Knowledge and Data Engineering (2021). https:\/\/doi.org\/10.1109\/TKDE.2021.3070203","DOI":"10.1109\/TKDE.2021.3070203"},{"key":"44_CR63","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"44_CR64","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Multi-task learning for segmentation and classification of tumors in 3D automated breast ultrasound images. Med. Image Anal. 70, 101918 (2021)","DOI":"10.1016\/j.media.2020.101918"},{"key":"44_CR65","doi-asserted-by":"crossref","unstructured":"Zhu, H., Salcudean, S., Rohling, R.: Gaze-guided class activation mapping: leveraging human attention for network attention in chest x-rays classification. arXiv preprint arXiv:2202.07107 (2022)","DOI":"10.1145\/3554944.3554952"},{"issue":"7","key":"44_CR66","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s11548-019-01964-8","volume":"14","author":"H Zhu","year":"2019","unstructured":"Zhu, H., Salcudean, S.E., Rohling, R.N.: A novel gaze-supported multimodal human-computer interaction for ultrasound machines. Int. J. Comput. Assist. Radiol. Surg. 14(7), 1107\u20131115 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12053-4_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T20:24:55Z","timestamp":1727641495000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12053-4_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031120527","9783031120534"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12053-4_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"25 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miua2022.com\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}