{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:33:51Z","timestamp":1757313231195,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031093418"},{"type":"electronic","value":"9783031093425"}],"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\/978-3-031-09342-5_26","type":"book-chapter","created":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:05:42Z","timestamp":1657285542000},"page":"273-282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Calibrating Histopathology Image Classifiers Using Label Smoothing"],"prefix":"10.1007","author":[{"given":"Jerry","family":"Wei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"Torresani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeed","family":"Hassanpour","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"issue":"2","key":"26_CR1","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/S1470-2045(19)30739-9","volume":"21","author":"W Bulten","year":"2020","unstructured":"Bulten, W., et al.: Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 21(2), 233\u2013241 (2020)","journal-title":"Lancet Oncol."},{"key":"26_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/978-3-030-01364-6_12","volume-title":"Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis","author":"V Cheplygina","year":"2018","unstructured":"Cheplygina, V., Pluim, J.P.W.: Crowd disagreement about medical images is informative. In: Stoyanov, D., et al. (eds.) LABELS\/CVII\/STENT -2018. LNCS, vol. 11043, pp. 105\u2013111. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01364-6_12"},{"key":"26_CR3","doi-asserted-by":"publisher","first-page":"2388","DOI":"10.1016\/S0140-6736(18)31645-3","volume":"392","author":"S Chilamkurthy","year":"2018","unstructured":"Chilamkurthy, S., et al.: Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet 392, 2388\u20132396 (2018)","journal-title":"Lancet"},{"key":"26_CR4","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2017","unstructured":"Coudray, N., Moreira, A.L., Sakellaropoulos, T., Feny\u00f6, D., Razavian, N., Tsirigos, A.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559\u20131567 (2017)","journal-title":"Nat. Med."},{"issue":"22","key":"26_CR5","doi-asserted-by":"publisher","first-page":"2199","DOI":"10.1001\/jama.2017.14585","volume":"318","author":"B Ehteshami Bejnordi","year":"2017","unstructured":"Ehteshami Bejnordi, B., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318(22), 2199\u20132210 (2017)","journal-title":"JAMA"},{"key":"26_CR6","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017)","journal-title":"Nature"},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1097\/PAS.0b013e318093e40a","volume":"32","author":"AB Farris","year":"2008","unstructured":"Farris, A.B., et al.: Sessile serrated adenoma: challenging discrimination from other serrated colonic polyps. Am. J. Surg. Pathol. 32, 30\u201335 (2008)","journal-title":"Am. J. Surg. Pathol."},{"key":"26_CR8","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1309\/NXDB6FMTE9X5CD6Y","volume":"127","author":"K Glatz","year":"2007","unstructured":"Glatz, K., Pritt, B., Glatz, D., Hartmann, A., O\u2019Brien, M.J., Glaszyk, H.: A multinational, internet-based assessment of observer variability in the diagnosis of serrated colorectal polyps. Am. J. Clin. Pathol. 127, 938\u2013945 (2007)","journal-title":"Am. J. Clin. Pathol."},{"key":"26_CR9","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402\u20132410 (2016)","journal-title":"JAMA"},{"key":"26_CR10","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: ICML (2017)"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"26_CR12","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: Deep Learning and Representation Learning Workshop at NeurIPS 2014 (2015)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Irvin, J., Rajpurkar, P., et al.: CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: AAAI (2019)","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"F Kanavati","year":"2020","unstructured":"Kanavati, F., et al.: Weakly-supervised learning for lung carcinoma classification using deep learning. Nat. Sci. Rep. 10, 1\u201311 (2020)","journal-title":"Nat. Sci. Rep."},{"key":"26_CR15","doi-asserted-by":"publisher","first-page":"30","DOI":"10.4103\/jpi.jpi_34_17","volume":"8","author":"B Korbar","year":"2017","unstructured":"Korbar, B., et al.: Deep learning for classification of colorectal polyps on whole-slide images. J. Pathol. Inform. 8, 30 (2017)","journal-title":"J. Pathol. Inform."},{"key":"26_CR16","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.: When does label smoothing help? In: NeurIPS (2020)"},{"key":"26_CR17","unstructured":"Naeini, M.P., Cooper, G., Hauskrecht, M.: Obtaining well calibrated probabilities using Bayesian binning. In: AAAI (2015)"},{"key":"26_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1007\/978-3-030-32226-7_68","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"X Ouyang","year":"2019","unstructured":"Ouyang, X., et al.: Weakly supervised segmentation framework with uncertainty: a study on pneumothorax segmentation in chest X-ray. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 613\u2013621. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_68"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Papernot, N., McDaniel, P., Wu, X., Jha, S., Swami, A.: Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE Symposium on Security and Privacy (SP), pp. 582\u2013597 (2016)","DOI":"10.1109\/SP.2016.41"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Peterson, J., Battleday, R., Griffiths, T., Russakovsky, O.: Human uncertainty makes classification more robust. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00971"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Pham, H.H., Le, T.T., Tran, D.Q., Ngo, D.T., Nguyen, H.Q.: Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels. In: MIDL 2020 - Medical Imaging with Deep Learning (2020)","DOI":"10.1101\/19013342"},{"key":"26_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"L Shen","year":"2019","unstructured":"Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E., McBride, R., Sieh, W.: Deep learning to improve breast cancer detection on screening mammography. Nat. Sci. Rep. 9, 1\u201312 (2019)","journal-title":"Nat. Sci. Rep."},{"issue":"5","key":"26_CR23","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1183\/09031936.00219211","volume":"40","author":"A Warth","year":"2012","unstructured":"Warth, A., et al.: Interobserver variability in the application of the novel IASLC\/ATS\/ERS classification for pulmonary adenocarcinomas. Eur. Respir. J. 40(5), 1221\u20131227 (2012)","journal-title":"Eur. Respir. J."},{"key":"26_CR24","doi-asserted-by":"crossref","unstructured":"Wei, J., et al.: Learn like a pathologist: curriculum learning by annotator agreement for histopathology image classification. In: Winter Conference on Applications of Computer Vision (WACV) (2020)","DOI":"10.1109\/WACV48630.2021.00252"},{"key":"26_CR25","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1007\/978-3-030-77211-6_2","volume-title":"Artificial Intelligence in Medicine","author":"J Wei","year":"2021","unstructured":"Wei, J., et al.: A petri dish for histopathology image analysis. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Ria\u00f1o, D. (eds.) AIME 2021. LNCS (LNAI), vol. 12721, pp. 11\u201324. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77211-6_2"},{"key":"26_CR26","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1111\/j.1365-2559.2009.03329.x","volume":"55","author":"N Wong","year":"2009","unstructured":"Wong, N., Hunt, L., Novelli, M., Shepherd, N., Warren, B.: Observer agreement in the diagnosis of serrated polyps of the large bowel. Histopathology 55, 63\u201366 (2009)","journal-title":"Histopathology"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09342-5_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T13:09:06Z","timestamp":1657285746000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09342-5_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031093418","9783031093425"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09342-5_26","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":"9 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Halifax, NS","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","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":"14 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aime22.aimedicine.info\/","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":"113","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":"39","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":"7","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":"35% - 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)"}}]}}