{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:12:39Z","timestamp":1742980359586,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031048807"},{"type":"electronic","value":"9783031048814"}],"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\/978-3-031-04881-4_10","type":"book-chapter","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T19:02:54Z","timestamp":1650913374000},"page":"119-128","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Increased Robustness in\u00a0Chest X-Ray Classification Through Clinical Report-Driven Regularization"],"prefix":"10.1007","author":[{"given":"Diogo","family":"Mata","sequence":"first","affiliation":[]},{"given":"Wilson","family":"Silva","sequence":"additional","affiliation":[]},{"given":"Jaime S.","family":"Cardoso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,26]]},"reference":[{"issue":"2","key":"10_CR1","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423\u2013443 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR2","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2019)"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Goldberger, A., et al.: Components of a new research resource for complex physiologic signals. PhysioNet 101 (2000)","DOI":"10.1161\/01.CIR.101.23.e215"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks (2018)","DOI":"10.1109\/CVPR.2017.243"},{"key":"10_CR5","unstructured":"Johnson, A.E.W., et al.: MIMIC-CXR-JPG: a large publicly available database of labeled chest radiographs. CoRR abs\/1901.07042 (2019). http:\/\/arxiv.org\/abs\/1901.07042"},{"key":"10_CR6","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"10_CR7","unstructured":"Kokhlikyan, N., et al.: Captum: A unified and generic model interpretability library for PyTorch (2020)"},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"Li, Y., Tian, S., Huang, Y., Dong, W.: Driverless artificial intelligence framework for the identification of malignant pleural effusion. Transl. Oncol. 14(1), 100896 (2021). https:\/\/doi.org\/10.1016\/j.tranon.2020.100896. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1936523320303880","DOI":"10.1016\/j.tranon.2020.100896"},{"key":"10_CR9","unstructured":"Lucieri, A., Dengel, A., Ahmed, S.: Deep learning based decision support for medicine - a case study on skin cancer diagnosis (2021)"},{"key":"10_CR10","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: International Conference on Machine Learning, pp. 3145\u20133153. PMLR (2017)"},{"key":"10_CR11","doi-asserted-by":"publisher","unstructured":"Silva, W., Poellinger, A., Cardoso, J.S., Reyes, M.: Interpretability-guided content-based medical image retrieval. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 305\u2013314. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_30","DOI":"10.1007\/978-3-030-59710-8_30"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 4793\u20134813 (2021)","DOI":"10.1109\/TNNLS.2020.3027314"},{"key":"10_CR13","doi-asserted-by":"publisher","unstructured":"Yu, Y., Hu, P., Lin, J., Krishnaswamy, P.: Multimodal multitask deep learning for X-ray image retrieval. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12905, pp. 603\u2013613. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87240-3_58","DOI":"10.1007\/978-3-030-87240-3_58"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-04881-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:01:17Z","timestamp":1709827277000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-04881-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031048807","9783031048814"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-04881-4_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"26 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IbPRIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberian Conference on Pattern Recognition and Image Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aveiro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"4 May 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 May 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ibpria2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ibpria.org\/2022\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","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":"54","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":"75% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}