{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T02:53:36Z","timestamp":1771296816674,"version":"3.50.1"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030611651","type":"print"},{"value":"9783030611668","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-61166-8_8","type":"book-chapter","created":{"date-parts":[[2020,10,3]],"date-time":"2020-10-03T15:02:50Z","timestamp":1601737370000},"page":"73-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Explainability for Regression CNN in Fetal Head Circumference Estimation from Ultrasound Images"],"prefix":"10.1007","author":[{"given":"Jing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caroline","family":"Petitjean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Florian","family":"Yger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samia","family":"Ainouz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,2]]},"reference":[{"key":"8_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Advances in Neural Information Processing Systems, pp. 9505\u20139515 (2018)"},{"key":"8_CR2","unstructured":"Alber, M., et al.: Investigate neural networks! CoRR abs\/1808.04260 (2018). http:\/\/arxiv.org\/abs\/1808.04260"},{"issue":"7","key":"8_CR3","doi-asserted-by":"publisher","first-page":"e0130140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PloS One 10(7), e0130140 (2015)","journal-title":"PloS One"},{"key":"8_CR4","unstructured":"Balduzzi, D., McWilliams, B., Butler-Yeoman, T.: Neural Taylor approximations: convergence and exploration in rectifier networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 351\u2013360. JMLR.org (2017)"},{"key":"8_CR5","doi-asserted-by":"publisher","unstructured":"Fong, R., Vedaldi, A.: Interpretable explanations of black boxes by meaningful perturbation. In: IEEE International Conference on Computer Vision (ICCV), pp. 3449\u20133457, October 2017. https:\/\/doi.org\/10.1109\/ICCV.2017.371. http:\/\/arxiv.org\/abs\/1704.03296, arXiv: 1704.03296","DOI":"10.1109\/ICCV.2017.371"},{"key":"8_CR6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"8","key":"8_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0200412","volume":"13","author":"TLA van den Heuvel","year":"2018","unstructured":"van den Heuvel, T.L.A., de Bruijn, D., de Korte, C.L., Ginneken, B.V.: Automated measurement of fetal head circumference using 2D ultrasound images. PlOs One 13(8), 1\u201320 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0200412","journal-title":"PlOs One"},{"key":"8_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2019.2946068","volume":"41","author":"S Lathuili\u00e8re","year":"2019","unstructured":"Lathuili\u00e8re, S., Mesejo, P., Alameda-Pineda, X., Horaud, R.: A comprehensive analysis of deep regression. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1\u201317 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"8_CR9","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.patcog.2016.11.008","volume":"65","author":"G Montavon","year":"2017","unstructured":"Montavon, G., Lapuschkin, S., Binder, A., Samek, W., M\u00fcller, K.R.: Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 65, 211\u2013222 (2017)","journal-title":"Pattern Recognit."},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Morch, N.J., et al.: Visualization of neural networks using saliency maps. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 2085\u20132090 (1995)","DOI":"10.1109\/ICNN.1995.488997"},{"issue":"11","key":"8_CR11","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.1109\/TNNLS.2016.2599820","volume":"28","author":"W Samek","year":"2016","unstructured":"Samek, W., Binder, A., Montavon, G., Lapuschkin, S., M\u00fcller, K.R.: Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2660\u20132673 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"8_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/978-3-030-28954-6_1","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"W Samek","year":"2019","unstructured":"Samek, W., M\u00fcller, K.-R.: Towards explainable artificial intelligence. In: Samek, W., Montavon, G., Vedaldi, A., Hansen, L.K., M\u00fcller, K.-R. (eds.) Explainable AI: Interpreting, Explaining and Visualizing Deep Learning. LNCS (LNAI), vol. 11700, pp. 5\u201322. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-28954-6_1"},{"key":"8_CR13","unstructured":"Shrikumar, A., Greenside, P., Shcherbina, A., Kundaje, A.: Not just a black box: learning important features through propagating activation differences. CoRR abs\/1605.01713 (2016). http:\/\/arxiv.org\/abs\/1605.01713"},{"key":"8_CR14","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. CoRR abs\/1312.6034 (2014)"},{"key":"8_CR15","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"issue":"6","key":"8_CR16","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3390\/jimaging6060052","volume":"6","author":"A Singh","year":"2020","unstructured":"Singh, A., Sengupta, S., Lakshminarayanan, V.: Explainable deep learning models in medical image analysis. J. Imaging 6(6), 52 (2020)","journal-title":"J. Imaging"},{"key":"8_CR17","unstructured":"Smilkov, D., Thorat, N., Kim, B., Vi\u00e9gas, F.B., Wattenberg, M.: SmoothGrad: removing noise by adding noise. In: Workshop on Visualization for Deep Learning, ICML (2017)"},{"key":"8_CR18","unstructured":"Springenberg, J., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. In: ICLR (Workshop Track) (2015). http:\/\/lmb.informatik.uni-freiburg.de\/Publications\/2015\/DB15a"},{"key":"8_CR19","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 3319\u20133328. JMLR.org (2017)"},{"key":"8_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"8_CR21","unstructured":"Zhang, J., Petitjean, C., Lopez, P., Ainouz, S.: Direct estimation of fetal head circumference from ultrasound images based on regression CNN. In: Medical Imaging with Deep Learning (2020)"},{"key":"8_CR22","unstructured":"Zintgraf, L.M., Cohen, T.S., Adel, T., Welling, M.: Visualizing deep neural network decisions: prediction difference analysis (2017). http:\/\/eprints.gla.ac.uk\/214152\/"}],"container-title":["Lecture Notes in Computer Science","Interpretable and Annotation-Efficient Learning for Medical Image Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61166-8_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:04:36Z","timestamp":1759442676000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61166-8_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030611651","9783030611668"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61166-8_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"2 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IMIMIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Interpretability of Machine Intelligence in Medical Image Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"imimic2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/imimic-workshop.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"50% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}