{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T20:41:24Z","timestamp":1793047284139,"version":"4.5.0"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164422","type":"print"},{"value":"9783031164439","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\/978-3-031-16443-9_67","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"701-713","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Explaining Chest X-Ray Pathologies in Natural Language"],"prefix":"10.1007","author":[{"given":"Maxime","family":"Kayser","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cornelius","family":"Emde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oana-Maria","family":"Camburu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guy","family":"Parsons","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bartlomiej","family":"Papiez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Lukasiewicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"67_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: NeurIPS (2018)"},{"key":"67_CR2","unstructured":"Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization (2005)"},{"key":"67_CR3","unstructured":"Bornstein, A.M.: Is artificial intelligence permanently inscrutable? (2016)"},{"key":"67_CR4","unstructured":"Camburu, O.M., Rockt\u00e4schel, T., Lukasiewicz, T., Blunsom, P.: e-SNLI: natural language inference with natural language explanations. In: NeurIPS (2018)"},{"key":"67_CR5","unstructured":"Cohen, J.P., et al.: TorchXRayVision: a library of chest X-ray datasets and models. arXiv preprint arXiv:2111.00595 (2020)"},{"key":"67_CR6","unstructured":"Gale, W., et al.: Producing radiologist-quality reports for interpretable artificial intelligence. arXiv preprint arXiv:1806.00340 (2018)"},{"issue":"11","key":"67_CR7","doi-asserted-by":"publisher","first-page":"e745","DOI":"10.1016\/S2589-7500(21)00208-9","volume":"3","author":"M Ghassemi","year":"2021","unstructured":"Ghassemi, M., Oakden-Rayner, L., Beam, A.L.: The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digital Health 3(11), e745\u2013e750 (2021)","journal-title":"Lancet Digital Health"},{"key":"67_CR8","unstructured":"Gu, J., Tresp, V.: Saliency methods for explaining adversarial attacks. arXiv preprint arXiv: 1908.08413 (2019)"},{"key":"67_CR9","doi-asserted-by":"crossref","unstructured":"Hajian, S., Bonchi, F., Castillo, C.: Algorithmic bias: from discrimination discovery to fairness-aware data mining. In: SIGKDD (2016)","DOI":"10.1145\/2939672.2945386"},{"issue":"8","key":"67_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"67_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-87234-2_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"B Hou","year":"2021","unstructured":"Hou, B., Kaissis, G., Summers, R.M., Kainz, B.: RATCHET: medical transformer for\u00a0chest X-ray diagnosis and reporting. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12907, pp. 293\u2013303. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87234-2_28"},{"key":"67_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"67_CR13","doi-asserted-by":"crossref","unstructured":"Irvin, J., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison, vol. 33(01) (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"67_CR14","unstructured":"Johnson, A.E.W., et al.: MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv:1901.07042 (2019)"},{"key":"67_CR15","doi-asserted-by":"crossref","unstructured":"Kayser, M., et al.: e-ViL: a dataset and benchmark for natural language explanations in vision-language tasks. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00128"},{"key":"67_CR16","doi-asserted-by":"crossref","unstructured":"Kim, E., Kim, S., Seo, M., Yoon, S.: XProtoNet: diagnosis in chest radiography with global and local explanations. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.01546"},{"key":"67_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/978-3-030-01216-8_35","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Kim","year":"2018","unstructured":"Kim, J., Rohrbach, A., Darrell, T., Canny, J., Akata, Z.: Textual explanations for self-driving vehicles. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 577\u2013593. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_35"},{"key":"67_CR18","unstructured":"Koh, P.W., et al.: Concept bottleneck models. In: ICML (2020)"},{"key":"67_CR19","doi-asserted-by":"crossref","unstructured":"Kotonya, N., Toni, F.: Explainable automated fact-checking for public health claims. In: EMNLP (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.623"},{"issue":"8","key":"67_CR20","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.1093\/jamia\/ocab046","volume":"28","author":"V Kougia","year":"2021","unstructured":"Kougia, V., et al.: RTEX: a novel framework for ranking, tagging, and explanatory diagnostic captioning of radiography exams. J. Am. Med. Inform. Assoc. 28(8), 1651\u20131659 (2021)","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"67_CR21","doi-asserted-by":"crossref","unstructured":"Langlotz, C.P., et al.: A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH\/RSNA\/ACR\/The Academy Workshop. Radiology 291(3) (2019)","DOI":"10.1148\/radiol.2019190613"},{"key":"67_CR22","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, S.T., Ro, Y.M.: Generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis. In: Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support (2019)","DOI":"10.1007\/978-3-030-33850-3_3"},{"key":"67_CR23","unstructured":"Majumder, B.P., Camburu, O.M., Lukasiewicz, T., McAuley, J.: Knowledge-grounded self-rationalization via extractive and natural language explanations. In: ICML (2022)"},{"key":"67_CR24","doi-asserted-by":"crossref","unstructured":"Marasovi\u0107, A., et al.: Natural language rationales with full-stack visual reasoning: from pixels to semantic frames to commonsense graphs. In: EMNLP Findings (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.253"},{"key":"67_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1\u201338 (2019)","journal-title":"Artif. Intell."},{"issue":"6","key":"67_CR26","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1109\/JBHI.2014.2344095","volume":"19","author":"M Mozaffari-Kermani","year":"2015","unstructured":"Mozaffari-Kermani, M., et al.: Systematic poisoning attacks on and defenses for machine learning in healthcare. IEEE J. Biomed. Health Inform. 19(6), 1893\u20131905 (2015)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"6464","key":"67_CR27","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","volume":"366","author":"Z Obermeyer","year":"2019","unstructured":"Obermeyer, Z., et al.: Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464), 447\u2013453 (2019)","journal-title":"Science"},{"key":"67_CR28","unstructured":"Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016)"},{"key":"67_CR29","doi-asserted-by":"crossref","unstructured":"Park, D.H., et al.: Multimodal explanations: justifying decisions and pointing to the evidence. In: ICCV (2018)","DOI":"10.1109\/CVPR.2018.00915"},{"key":"67_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/978-3-030-59710-8_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"E Puyol-Ant\u00f3n","year":"2020","unstructured":"Puyol-Ant\u00f3n, E., et al.: Interpretable deep models for cardiac resynchronisation therapy response prediction. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 284\u2013293. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_28"},{"key":"67_CR31","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language Models are Unsupervised Multitask Learners. openai.com (2019)"},{"key":"67_CR32","doi-asserted-by":"crossref","unstructured":"Rajani, N.F., McCann, B., Xiong, C., Socher, R.: Explain yourself! Leveraging language models for commonsense reasoning. In: ACL (2019)","DOI":"10.18653\/v1\/P19-1487"},{"key":"67_CR33","unstructured":"Schutte, K., Moindrot, O., H\u00e9rent, P., Schiratti, J.B., J\u00e9gou, S.: Using StyleGAN for visual interpretability of deep learning models on medical images. arXiv preprint arXiv:2101.07563 (2021)"},{"key":"67_CR34","doi-asserted-by":"crossref","unstructured":"Smit, A., et al.: Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. In: EMNLP (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.117"},{"key":"67_CR35","unstructured":"Stacey, J., Belinkov, Y., Rei, M.: Natural language inference with a human touch: using human explanations to guide model attention. In: AAAI (2022)"},{"key":"67_CR36","doi-asserted-by":"crossref","unstructured":"Vayena, E., Blasimme, A., Cohen, I.G.: Machine learning in medicine: addressing ethical challenges. PLoS Med. 15(11), e1002689 (2018)","DOI":"10.1371\/journal.pmed.1002689"},{"key":"67_CR37","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: TieNet: text-image embedding network for common thorax disease classification and reporting in chest X-rays. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00943"},{"key":"67_CR38","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: BERTScore: evaluating text generation with BERT. In: ICLR (2019)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16443-9_67","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:51:25Z","timestamp":1709830285000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16443-9_67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164422","9783031164439"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16443-9_67","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":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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":"5","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)"}}]}}