{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T18:07:28Z","timestamp":1763662048819,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031438974"},{"type":"electronic","value":"9783031438981"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43898-1_43","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"442-452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Data AUDIT: Identifying Attribute Utility- and\u00a0Detectability-Induced Bias in\u00a0Task Models"],"prefix":"10.1007","author":[{"given":"Mitchell","family":"Pavlak","sequence":"first","affiliation":[]},{"given":"Nathan","family":"Drenkow","sequence":"additional","affiliation":[]},{"given":"Nicholas","family":"Petrick","sequence":"additional","affiliation":[]},{"given":"Mohammad Mehdi","family":"Farhangi","sequence":"additional","affiliation":[]},{"given":"Mathias","family":"Unberath","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Aka, O., Burke, K., Bauerle, A., Greer, C., Mitchell, M.: Measuring model biases in the absence of ground truth. In: AAAI\/ACM AIES. ACM (2021)","key":"43_CR1","DOI":"10.1145\/3461702.3462557"},{"unstructured":"Bevan, P., Atapour-Abarghouei, A.: Skin deep unlearning: artefact and instrument debiasing in the context of melanoma classification (2021)","key":"43_CR2"},{"doi-asserted-by":"crossref","unstructured":"Bissoto, A., Fornaciali, M., Valle, E., Avila, S.: (De) constructing bias on skin lesion datasets. In: IEEE CVPRW (2019)","key":"43_CR3","DOI":"10.1109\/CVPRW.2019.00335"},{"doi-asserted-by":"crossref","unstructured":"Bissoto, A., Valle, E., Avila, S.: Debiasing skin lesion datasets and models? Not so fast. In: IEEE CVPRW, pp. 740\u2013741 (2020)","key":"43_CR4","DOI":"10.1109\/CVPRW50498.2020.00378"},{"key":"43_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.suc.2019.09.005","volume":"100","author":"S Carr","year":"2020","unstructured":"Carr, S., Smith, C., Wernberg, J.: Epidemiology and risk factors of melanoma. Surg. Clin. North Am. 100, 1\u201312 (2020)","journal-title":"Surg. Clin. North Am."},{"key":"43_CR6","doi-asserted-by":"publisher","first-page":"3673","DOI":"10.1038\/s41467-020-17478-w","volume":"11","author":"DC Castro","year":"2020","unstructured":"Castro, D.C., Walker, I., Glocker, B.: Causality matters in medical imaging. Nat. Commun. 11, 3673 (2020). https:\/\/doi.org\/10.1038\/s41467-020-17478-w","journal-title":"Nat. Commun."},{"key":"43_CR7","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1038\/s42256-021-00338-7","volume":"3","author":"AJ DeGrave","year":"2021","unstructured":"DeGrave, A.J., Janizek, J.D., Lee, S.I.: Ai for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3, 610\u2013619 (2021). https:\/\/doi.org\/10.1038\/s42256-021-00338-7","journal-title":"Nat. Mach. Intell."},{"key":"43_CR8","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":"43_CR9","doi-asserted-by":"publisher","first-page":"103552","DOI":"10.1016\/j.cviu.2022.103552","volume":"223","author":"S Fabbrizzi","year":"2022","unstructured":"Fabbrizzi, S., Papadopoulos, S., Ntoutsi, E., Kompatsiaris, I.: A survey on bias in visual datasets. Comput. Vis. Image Underst. 223, 103552 (2022)","journal-title":"Comput. Vis. Image Underst."},{"key":"43_CR10","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/s42256-020-00257-z","volume":"2","author":"R Geirhos","year":"2020","unstructured":"Geirhos, R., et al.: Shortcut learning in deep neural networks. Nat. Mach. Intell. 2, 665\u2013673 (2020)","journal-title":"Nat. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Gichoya, J.W., et al.: AI recognition of patient race in medical imaging: a modelling study. Lancet Digit. Health 4, e406\u2013e414 (2022)","key":"43_CR11","DOI":"10.1016\/S2589-7500(22)00063-2"},{"unstructured":"Glocker, B., Jones, C., Bernhardt, M., Winzeck, S.: Algorithmic encoding of protected characteristics in image-based models for disease detection (2021)","key":"43_CR12"},{"unstructured":"Glocker, B., Jones, C., Bernhardt, M., Winzeck, S.: Risk of bias in chest x-ray foundation models, September 2022","key":"43_CR13"},{"key":"43_CR14","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"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE\/CVPR, pp. 770\u2013778 (2016)","key":"43_CR15","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Henry Hinnefeld, J., Cooman, P., Mammo, N., Deese, R.: Evaluating fairness metrics in the presence of dataset bias, September 2018","key":"43_CR16"},{"unstructured":"Jabbour, S., Fouhey, D., Kazerooni, E., Sjoding, M.W., Wiens, J.: Deep learning applied to chest X-rays: exploiting and preventing shortcuts. In: Machine Learning for Healthcare Conference, pp. 750\u2013782. PMLR (2020)","key":"43_CR17"},{"key":"43_CR18","doi-asserted-by":"publisher","first-page":"e384","DOI":"10.1016\/S2589-7500(22)00003-6","volume":"4","author":"X Liu","year":"2022","unstructured":"Liu, X., Glocker, B., McCradden, M.M., Ghassemi, M., Denniston, A.K., Oakden-Rayner, L.: The medical algorithmic audit. Lancet Digit Health 4, e384\u2013e397 (2022)","journal-title":"Lancet Digit Health"},{"doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: IEEE\/CVPR (2021)","key":"43_CR19","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"43_CR20","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1136\/neurintsurg-2019-015135","volume":"12","author":"NM Murray","year":"2020","unstructured":"Murray, N.M., Unberath, M., Hager, G.D., Hui, F.K.: Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review. J. NeuroInterv. Surg. 12, 156\u2013164 (2020)","journal-title":"J. NeuroInterv. Surg."},{"doi-asserted-by":"crossref","unstructured":"Oakden-Rayner, L., Dunnmon, J., Carneiro, G., R\u00e9, C.: Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In: Proceedings of the ACM Conference on Health, Inference, and Learning, pp. 151\u2013159 (2020)","key":"43_CR21","DOI":"10.1145\/3368555.3384468"},{"doi-asserted-by":"crossref","unstructured":"O\u2019Brien, M., Bukowski, J., Hager, G., Pezeshk, A., Unberath, M.: Evaluating neural network robustness for melanoma classification using mutual information. In: Medical Imaging 2022: Image Processing. SPIE (2022)","key":"43_CR22","DOI":"10.1117\/12.2612192"},{"doi-asserted-by":"crossref","unstructured":"Raji, I.D., Kumar, I.E., Horowitz, A., Selbst, A.: The fallacy of AI functionality. In: ACM Conference on Fairness, Accountability, and Transparency. ACM (2022)","key":"43_CR23","DOI":"10.1145\/3531146.3533158"},{"unstructured":"Rajpurkar, P., et al.: CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)","key":"43_CR24"},{"doi-asserted-by":"crossref","unstructured":"Reimers, C., Penzel, N., Bodesheim, P., Runge, J., Denzler, J.: Conditional dependence tests reveal the usage of ABCD rule features and bias variables in automatic skin lesion classification. In: IEEE CVPRW (2021)","key":"43_CR25","DOI":"10.1109\/CVPRW53098.2021.00200"},{"key":"43_CR26","doi-asserted-by":"publisher","first-page":"075310","DOI":"10.1063\/1.5025050","volume":"28","author":"J Runge","year":"2018","unstructured":"Runge, J.: Causal network reconstruction from time series: from theoretical assumptions to practical estimation. Chaos 28, 075310 (2018)","journal-title":"Chaos"},{"unstructured":"Runge, J.: Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information. In: AISTATS. PMLR (2018)","key":"43_CR27"},{"doi-asserted-by":"crossref","unstructured":"Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., Aroyo, L.M.: \u201ceveryone wants to do the model work, not the data work\u201d: data cascades in high-stakes AI. In: ACM CHI. ACM (2021)","key":"43_CR28","DOI":"10.1145\/3411764.3445518"},{"unstructured":"Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I.Y., Ghassemi, M.: CheXclusion: fairness gaps in deep chest X-ray classifiers. In: Pacific Symposium on Biocomputing (2021)","key":"43_CR29"},{"key":"43_CR30","doi-asserted-by":"publisher","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","volume":"27","author":"L Seyyed-Kalantari","year":"2021","unstructured":"Seyyed-Kalantari, L., Zhang, H., McDermott, M.B.A., Chen, I.Y., Ghassemi, M.: Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat. Med. 27, 2176\u20132182 (2021)","journal-title":"Nat. Med."},{"key":"43_CR31","doi-asserted-by":"publisher","first-page":"eabb3652","DOI":"10.1126\/scitranslmed.abb3652","volume":"13","author":"LR Soenksen","year":"2021","unstructured":"Soenksen, L.R., et al.: Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images. Sci. Transl. Med. 13, eabb3652 (2021)","journal-title":"Sci. Transl. Med."},{"issue":"2","key":"43_CR32","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1002\/ima.22827","volume":"33","author":"E Somfai","year":"2023","unstructured":"Somfai, E., et al.: Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images. Int. J. Imaging Syst. Technol. 33(2), 556\u2013571 (2023)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"43_CR33","doi-asserted-by":"publisher","first-page":"180161","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018)","journal-title":"Sci. Data"},{"key":"43_CR34","first-page":"2837","volume":"11","author":"NX Vinh","year":"2010","unstructured":"Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. JMLR 11, 2837\u20132854 (2010)","journal-title":"JMLR"},{"key":"43_CR35","doi-asserted-by":"publisher","first-page":"1135","DOI":"10.1001\/jamadermatol.2019.1735","volume":"155","author":"JK Winkler","year":"2019","unstructured":"Winkler, J.K., et al.: Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 155, 1135\u20131141 (2019)","journal-title":"JAMA Dermatol."},{"key":"43_CR36","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ejca.2020.12.010","volume":"145","author":"JK Winkler","year":"2021","unstructured":"Winkler, J.K., et al.: Association between different scale bars in dermoscopic images and diagnostic performance of a market-approved deep learning convolutional neural network for melanoma recognition. Eur. J. Cancer 145, 146\u2013154 (2021)","journal-title":"Eur. J. Cancer"},{"unstructured":"Wyden, R., Booker, C., Clarke, Y.: Algorithmic accountability act of 2022 (2022)","key":"43_CR37"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43898-1_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:25:09Z","timestamp":1710167109000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43898-1_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438974","9783031438981"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43898-1_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","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":"Vancouver, BC","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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)"}}]}}