{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T22:14:27Z","timestamp":1784585667873,"version":"3.55.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030871987","type":"print"},{"value":"9783030871994","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87199-4_49","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"519-528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Using Causal Analysis for Conceptual Deep Learning Explanation"],"prefix":"10.1007","author":[{"given":"Sumedha","family":"Singla","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Wallace","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia","family":"Triantafillou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kayhan","family":"Batmanghelich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"49_CR1","doi-asserted-by":"crossref","unstructured":"Basu, S., Mitra, S., Saha, N.: Deep learning for screening COVID-19 using chest X-ray images. In: IEEE Symposium Series on Computational Intelligence (SSCI) (2020)","DOI":"10.1101\/2020.05.04.20090423"},{"key":"49_CR2","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 6541\u20136549 (2017)","DOI":"10.1109\/CVPR.2017.354"},{"issue":"48","key":"49_CR3","doi-asserted-by":"publisher","first-page":"30071","DOI":"10.1073\/pnas.1907375117","volume":"117","author":"D Bau","year":"2020","unstructured":"Bau, D., Zhu, J.Y., Strobelt, H., Lapedriza, A., Zhou, B., Torralba, A.: Understanding the role of individual units in a deep neural network. Nat. Acad. Sci. 117(48), 30071\u201330078 (2020)","journal-title":"Nat. Acad. Sci."},{"key":"49_CR4","doi-asserted-by":"crossref","unstructured":"Clough, J.R., Oksuz, I., Puyol-Ant\u00f3n, E., Ruijsink, B., King, A.P., Schnabel, J.A.: Global and local interpretability for cardiac MRI classification. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 656\u2013664 (2019)","DOI":"10.1007\/978-3-030-32251-9_72"},{"key":"49_CR5","doi-asserted-by":"crossref","unstructured":"Glass, A., McGuinness, D.L., Wolverton, M.: Toward establishing trust in adaptive agents. In: International Conference on Intelligent User Interfaces (2008)","DOI":"10.1145\/1378773.1378804"},{"key":"49_CR6","doi-asserted-by":"publisher","first-page":"103865","DOI":"10.1016\/j.compbiomed.2020.103865","volume":"123","author":"M Graziani","year":"2020","unstructured":"Graziani, M., Andrearczyk, V., Marchand-Maillet, S., M\u00fcller, H.: Concept attribution: explaining CNN decisions to physicians. Comput. Biol. Med. 123, 103865 (2020)","journal-title":"Comput. Biol. Med."},{"key":"49_CR7","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE Computer Vision and Pattern Recognition (CVPR), pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"issue":"5","key":"49_CR8","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1080\/00273171.2011.606743","volume":"46","author":"K Imai","year":"2011","unstructured":"Imai, K., Jo, B., Stuart, E.A.: Commentary: using potential outcomes to understand causal mediation analysis. Multivar. Behav. Res. 46(5), 861\u2013873 (2011)","journal-title":"Multivar. Behav. Res."},{"key":"49_CR9","first-page":"590","volume":"33","author":"J Irvin","year":"2019","unstructured":"Irvin, J., et al.: Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. AAAI Conf. Artif. Intell. 33, 590\u2013597 (2019)","journal-title":"AAAI Conf. Artif. Intell."},{"issue":"1","key":"49_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-019-0322-0","volume":"6","author":"AE Johnson","year":"2019","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)","journal-title":"Sci. Data"},{"key":"49_CR11","doi-asserted-by":"publisher","first-page":"31","DOI":"10.2147\/OAEM.S29942","volume":"4","author":"VS Karkhanis","year":"2012","unstructured":"Karkhanis, V.S., Joshi, J.M.: Pleural effusion: diagnosis, treatment, and management. Open Access Emerg. Med. (OAEM) 4, 31 (2012)","journal-title":"Open Access Emerg. Med. (OAEM)"},{"key":"49_CR12","unstructured":"Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In: International Conference on Machine Learning (ICML), pp. 2668\u20132677 (2018)"},{"key":"49_CR13","first-page":"4765","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural. Inf. Process. Syst. 30, 4765\u20134774 (2017)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"49_CR14","doi-asserted-by":"publisher","first-page":"879","DOI":"10.2214\/ajr.144.5.879","volume":"144","author":"E Milne","year":"1985","unstructured":"Milne, E., Pistolesi, M., Miniati, M., Giuntini, C.: The radiologic distinction of cardiogenic and noncardiogenic edema. Am. J. Roentgenol. 144(5), 879\u2013894 (1985)","journal-title":"Am. J. Roentgenol."},{"key":"49_CR15","doi-asserted-by":"crossref","unstructured":"Nakamori, N., MacMahon, H., Sasaki, Y., Montner, S., et al.: Effect of heart-size parameters computed from digital chest radiographs on detection of cardiomegaly. potential usefulness for computer-aided diagnosis. Invest. Radiol. 26(6), 546\u2013550 (1991)","DOI":"10.1097\/00004424-199106000-00008"},{"key":"49_CR16","unstructured":"Pearl, J.: Direct and indirect effects. In: Conference on Uncertainty and Artificial Intelligence (UAI), pp. 411\u2013420 (2001)"},{"key":"49_CR17","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? explaining the predictions of any classifier. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"5","key":"49_CR18","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1037\/h0037350","volume":"66","author":"DB Rubin","year":"1974","unstructured":"Rubin, D.B.: Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol. 66(5), 688 (1974)","journal-title":"J. Educ. Psychol."},{"key":"49_CR19","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: International Conference on Computer Vision (ICCV), pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"49_CR20","unstructured":"Singla, S., Pollack, B., Chen, J., Batmanghelich, K.: Explanation by progressive exaggeration. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"49_CR21","unstructured":"Vig, J., et al.: Investigating gender bias in language models using causal mediation analysis. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 12388\u201312401 (2020)"},{"key":"49_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-030-33850-3_2","volume-title":"Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support","author":"H Yeche","year":"2019","unstructured":"Yeche, H., Harrison, J., Berthier, T.: UBS: a dimension-agnostic metric for concept vector interpretability applied to radiomics. In: Suzuki, K., et al. (eds.) ML-CDS\/IMIMIC -2019. LNCS, vol. 11797, pp. 12\u201320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33850-3_2"},{"key":"49_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, B., Sun, Y., Bau, D., Torralba, A.: Interpretable basis decomposition for visual explanation. In: European Conference on Computer Vision (ECCV), pp. 119\u2013134 (2018)","DOI":"10.1007\/978-3-030-01237-3_8"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87199-4_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:35:57Z","timestamp":1632378957000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87199-4_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871987","9783030871994"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87199-4_49","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually.","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)"}}]}}