{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T14:13:52Z","timestamp":1776089632881,"version":"3.50.1"},"reference-count":84,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2145640"],"award-info":[{"award-number":["2145640"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","award":["R01LM014306"],"award-info":[{"award-number":["R01LM014306"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include \u201cnoisy label AND medical\/healthcare\/clinical,\u201d \u201cuncertainty AND medical\/healthcare\/clinical,\u201d and \u201cnoise AND medical\/healthcare\/clinical.\u201d<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae108","type":"journal-article","created":{"date-parts":[[2024,5,9]],"date-time":"2024-05-09T12:44:02Z","timestamp":1715258642000},"page":"1596-1607","source":"Crossref","is-referenced-by-count":20,"title":["Deep learning with noisy labels in medical prediction problems: a scoping review"],"prefix":"10.1093","volume":"31","author":[{"given":"Yishu","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine , New York, NY 10065,","place":["United States"]},{"name":"Reddit Inc. , San Francisco, CA 16093,","place":["United States"]}]},{"given":"Yu","family":"Deng","sequence":"additional","affiliation":[{"name":"Center for Health Information Partnerships, Northwestern University , Chicago, IL 10611,","place":["United States"]}]},{"given":"Cong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine , New York, NY 10065,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0862-6588","authenticated-orcid":false,"given":"Mingquan","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine , New York, NY 10065,","place":["United States"]},{"name":"Department of Surgery, University of Minnesota , Minneapolis, MN 55455,","place":["United States"]}]},{"given":"Hongmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, Northwestern University , Evanston, IL 60208,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9309-8331","authenticated-orcid":false,"given":"Yifan","family":"Peng","sequence":"additional","affiliation":[{"name":"Department of Population Health Sciences, Weill Cornell Medicine , New York, NY 10065,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"2025052920013574500_ocae108-B1","doi-asserted-by":"crossref","first-page":"105111","DOI":"10.1016\/j.compbiomed.2021.105111","article-title":"Transparency of deep neural networks for medical image analysis: a review of interpretability methods","volume":"140","author":"Salahuddin","year":"2022","journal-title":"Comput Biol Med"},{"issue":"4","key":"2025052920013574500_ocae108-B2","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.jaapos.2007.11.022","article-title":"Agreement among pediatric ophthalmologists in diagnosing plus and pre-plus disease in retinopathy of prematurity","volume":"12","author":"Wallace","year":"2008","journal-title":"J AAPOS"},{"key":"2025052920013574500_ocae108-B3","first-page":"23","volume-title":"The European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning","author":"Fr\u00e9nay","year":"2014"},{"issue":"11","key":"2025052920013574500_ocae108-B4","doi-asserted-by":"crossref","first-page":"8135","DOI":"10.1109\/TNNLS.2022.3152527","article-title":"Learning from noisy labels with deep neural networks: a survey","volume":"34","author":"Song","year":"2023","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2025052920013574500_ocae108-B5","doi-asserted-by":"crossref","first-page":"106771","DOI":"10.1016\/j.knosys.2021.106771","article-title":"Image classification with deep learning in the presence of noisy labels: a survey","volume":"215","author":"Algan","year":"2021","journal-title":"Knowledge-Based Syst"},{"issue":"2","key":"2025052920013574500_ocae108-B6","doi-asserted-by":"crossref","first-page":"021401","DOI":"10.1149\/2754-2726\/ac75f5","article-title":"Review\u2014a survey of learning from noisy labels","volume":"1","author":"Liang","year":"2022","journal-title":"ECS Sens Plus"},{"key":"2025052920013574500_ocae108-B7","doi-asserted-by":"crossref","first-page":"101759","DOI":"10.1016\/j.media.2020.101759","article-title":"Deep learning with noisy labels: exploring techniques and remedies in medical image analysis","volume":"65","author":"Karimi","year":"2020","journal-title":"Med Image Anal"},{"issue":"7","key":"2025052920013574500_ocae108-B8","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"issue":"9","key":"2025052920013574500_ocae108-B9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIP.2016.2588330","article-title":"Deep and structured robust information theoretic learning for image analysis","volume":"25","author":"Deng","year":"2016","journal-title":"IEEE Trans on Image Process"},{"key":"2025052920013574500_ocae108-B10","first-page":"39","author":"Dgani","year":"2018"},{"key":"2025052920013574500_ocae108-B11","first-page":"1280","author":"Xue","year":"2019"},{"issue":"6","key":"2025052920013574500_ocae108-B12","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/TMI.2022.3141425","article-title":"Improving medical images classification with label noise using dual-uncertainty estimation","volume":"41","author":"Ju","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"2025052920013574500_ocae108-B13","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1109\/TMI.2021.3140140","article-title":"Robust medical image classification from noisy labeled data with global and local representation guided co-training","volume":"41","author":"Xue","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"key":"2025052920013574500_ocae108-B14","author":"Jiang","year":"2023"},{"key":"2025052920013574500_ocae108-B15","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.neucom.2021.06.012","article-title":"Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images","volume":"458","author":"Hu","year":"2021","journal-title":"Neurocomputing (Amst)"},{"issue":"1","key":"2025052920013574500_ocae108-B16","doi-asserted-by":"crossref","first-page":"2596","DOI":"10.1038\/s41598-020-59315-6","article-title":"Fully automated plaque characterization in intravascular OCT images using hybrid convolutional and lumen morphology features","volume":"10","author":"Lee","year":"2020","journal-title":"Sci Rep"},{"issue":"5","key":"2025052920013574500_ocae108-B17","doi-asserted-by":"crossref","first-page":"e0285996","DOI":"10.1371\/journal.pone.0285996","article-title":"Accurate deep learning model using semi-supervised learning and noisy student for cervical cancer screening in low magnification images","volume":"18","author":"Kurita","year":"2023","journal-title":"PLoS One"},{"issue":"1","key":"2025052920013574500_ocae108-B18","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1038\/s41467-019-11012-3","article-title":"Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences","volume":"10","author":"Fries","year":"2019","journal-title":"Nat Commun"},{"issue":"1","key":"2025052920013574500_ocae108-B19","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s40478-022-01365-0","article-title":"Deep learning from multiple experts improves identification of amyloid neuropathologies","volume":"10","author":"Wong","year":"2022","journal-title":"Acta Neuropathol Commun"},{"issue":"1","key":"2025052920013574500_ocae108-B20","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1038\/s41598-022-05001-8","article-title":"A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network","volume":"12","author":"Ashraf","year":"2022","journal-title":"Sci Rep"},{"key":"2025052920013574500_ocae108-B21","doi-asserted-by":"crossref","first-page":"102370","DOI":"10.1016\/j.displa.2023.102370","article-title":"COVID-19 chest X-ray image classification in the presence of noisy labels","volume":"77","author":"Ying","year":"2023","journal-title":"Displays"},{"issue":"1","key":"2025052920013574500_ocae108-B22","doi-asserted-by":"crossref","first-page":"11612","DOI":"10.1038\/s41598-021-90821-3","article-title":"Learning from crowds in digital pathology using scalable variational Gaussian processes","volume":"11","author":"L\u00f3pez-P\u00e9rez","year":"2021","journal-title":"Sci Rep"},{"key":"2025052920013574500_ocae108-B23","first-page":"1910","author":"Karimi","year":"2020"},{"issue":"8","key":"2025052920013574500_ocae108-B24","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.3390\/diagnostics12082023","article-title":"Advancing brain metastases detection in T1-weighted contrast-enhanced 3D MRI using noisy student-based training","volume":"12","author":"Dikici","year":"2022","journal-title":"Diagnostics"},{"key":"2025052920013574500_ocae108-B25","doi-asserted-by":"crossref","first-page":"104704","DOI":"10.1016\/j.compbiomed.2021.104704","article-title":"Learning-to-augment strategy using noisy and denoised data: improving generalizability of deep CNN for the detection of COVID-19 in X-ray images","volume":"136","author":"Momeny","year":"2021","journal-title":"Comput Biol Med"},{"key":"2025052920013574500_ocae108-B26","first-page":"981","author":"Jaiswal","year":"2022"},{"key":"2025052920013574500_ocae108-B27","first-page":"563","author":"Pulido","year":"2020"},{"issue":"10","key":"2025052920013574500_ocae108-B28","doi-asserted-by":"crossref","first-page":"2642","DOI":"10.1109\/TMI.2021.3054817","article-title":"Generalized zero-shot chest x-ray diagnosis through trait-guided multi-view semantic embedding with self-training","volume":"40","author":"Paul","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"2025052920013574500_ocae108-B29","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1109\/TMI.2021.3135002","article-title":"Pathal: an active learning framework for histopathology image analysis","volume":"41","author":"Li","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"2025052920013574500_ocae108-B30","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.1109\/TBME.2018.2889915","article-title":"Reliable label-efficient learning for biomedical image recognition","volume":"66","author":"Gu","year":"2018","journal-title":"IEEE Trans Biomed Eng"},{"key":"2025052920013574500_ocae108-B31","doi-asserted-by":"crossref","first-page":"104711","DOI":"10.1016\/j.compbiomed.2021.104711","article-title":"REUR: a unified deep framework for signet ring cell detection in low-resolution pathological images","volume":"136","author":"Zhang","year":"2021","journal-title":"Comput Biol Med"},{"key":"2025052920013574500_ocae108-B32","doi-asserted-by":"crossref","first-page":"102087","DOI":"10.1016\/j.media.2021.102087","article-title":"Robust classification from noisy labels: integrating additional knowledge for chest radiography abnormality assessment","volume":"72","author":"G\u00fcndel","year":"2021","journal-title":"Med Image Anal"},{"key":"2025052920013574500_ocae108-B33","author":"Algan","year":"2020"},{"key":"2025052920013574500_ocae108-B34","first-page":"676","author":"Ghesu","year":"2019"},{"key":"2025052920013574500_ocae108-B35","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.neucom.2020.03.127","article-title":"Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels","volume":"437","author":"Pham","year":"2021","journal-title":"Neurocomputing"},{"key":"2025052920013574500_ocae108-B36","first-page":"590","author":"Irvin","year":"2019"},{"key":"2025052920013574500_ocae108-B37","first-page":"306","author":"Li","year":"2023"},{"key":"2025052920013574500_ocae108-B38","doi-asserted-by":"crossref","first-page":"105763","DOI":"10.1016\/j.compbiomed.2022.105763","article-title":"Adaptive cross entropy for ultrasmall object detection in computed tomography with noisy labels","volume":"147","author":"Chen","year":"2022","journal-title":"Comput Biol Med"},{"key":"2025052920013574500_ocae108-B39","doi-asserted-by":"crossref","first-page":"106340","DOI":"10.1016\/j.compbiomed.2022.106340","article-title":"Automatic diagnosis and grading of prostate cancer with weakly supervised learning on whole slide images","volume":"152","author":"Xiang","year":"2023","journal-title":"Comput Biol Med"},{"key":"2025052920013574500_ocae108-B40","doi-asserted-by":"crossref","first-page":"102231","DOI":"10.1016\/j.compmedimag.2023.102231","article-title":"Labeling confidence for uncertainty-aware histology image classification","volume":"107","author":"Del Amor","year":"2023","journal-title":"Comput Med Imaging Graph"},{"key":"2025052920013574500_ocae108-B41","first-page":"289","author":"Calli","year":"2019"},{"key":"2025052920013574500_ocae108-B42","first-page":"1","author":"Braun","year":"2022"},{"key":"2025052920013574500_ocae108-B43","doi-asserted-by":"crossref","first-page":"102273","DOI":"10.1016\/j.media.2021.102273","article-title":"Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty","volume":"75","author":"Jim\u00e9nez-S\u00e1nchez","year":"2022","journal-title":"Med Image Anal"},{"issue":"12","key":"2025052920013574500_ocae108-B44","doi-asserted-by":"crossref","first-page":"3580","DOI":"10.1109\/TMI.2021.3091178","article-title":"Co-correcting: noise-tolerant medical image classification via mutual label correction","volume":"40","author":"Liu","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"key":"2025052920013574500_ocae108-B45","doi-asserted-by":"crossref","first-page":"102278","DOI":"10.1016\/j.compmedimag.2023.102278","article-title":"A fundus image classification framework for learning with noisy labels","volume":"108","author":"Hu","year":"2023","journal-title":"Comput Med Imaging Graph"},{"key":"2025052920013574500_ocae108-B46","first-page":"3225","author":"Shi","year":"2021"},{"issue":"4","key":"2025052920013574500_ocae108-B47","doi-asserted-by":"crossref","first-page":"045018","DOI":"10.1088\/1361-6560\/acb481","article-title":"Clinical knowledge embedded method based on multi-task learning for thyroid nodule classification with ultrasound images","volume":"68","author":"Gao","year":"2023","journal-title":"Phys Med Biol"},{"issue":"9","key":"2025052920013574500_ocae108-B48","doi-asserted-by":"crossref","first-page":"5899","DOI":"10.1002\/mp.15799","article-title":"Bayesian statistics-guided label refurbishment mechanism: mitigating label noise in medical image classification","volume":"49","author":"Gao","year":"2022","journal-title":"Med Phys"},{"issue":"5","key":"2025052920013574500_ocae108-B49","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1007\/s11517-022-02743-5","article-title":"ReFixMatch-LS: reusing pseudo-labels for semi-supervised skin lesion classification","volume":"61","author":"Zhou","year":"2023","journal-title":"Med Biol Eng Comput"},{"issue":"4","key":"2025052920013574500_ocae108-B50","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s11548-022-02799-6","article-title":"Robust co-teaching learning with consistency-based noisy label correction for medical image classification","volume":"18","author":"Zhu","year":"2023","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"9","key":"2025052920013574500_ocae108-B51","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1007\/s11548-022-02707-y","article-title":"Training deep neural networks with noisy clinical labels: toward accurate detection of prostate cancer in US data","volume":"17","author":"Javadi","year":"2022","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2025052920013574500_ocae108-B52","first-page":"562","author":"Chen","year":"2023"},{"key":"2025052920013574500_ocae108-B53","first-page":"21284","author":"Chen","year":"2023"},{"key":"2025052920013574500_ocae108-B54","author":"Boughorbel","year":"2018"},{"key":"2025052920013574500_ocae108-B55","first-page":"2023","author":"Yang","year":"2023"},{"issue":"1","key":"2025052920013574500_ocae108-B56","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1093\/jamia\/ocy154","article-title":"Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling","volume":"26","author":"Murray","year":"2019","journal-title":"J Am Med Inform Assoc"},{"issue":"1","key":"2025052920013574500_ocae108-B57","doi-asserted-by":"crossref","first-page":"ooac107","DOI":"10.1093\/jamiaopen\/ooac107","article-title":"Not so weak PICO: leveraging weak supervision for participants, interventions, and outcomes recognition for systematic review automation","volume":"6","author":"Dhrangadhariya","year":"2023","journal-title":"JAMIA Open"},{"issue":"3","key":"2025052920013574500_ocae108-B58","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1162\/dint_a_00099","article-title":"Semi-supervised noisy label learning for Chinese clinical named entity recognition","volume":"3","author":"Li","year":"2021","journal-title":"Data Intelligence"},{"issue":"9","key":"2025052920013574500_ocae108-B59","doi-asserted-by":"crossref","first-page":"094001","DOI":"10.1088\/1361-6579\/ac89cb","article-title":"Label noise and self-learning label correction in cardiac abnormalities classification","volume":"43","author":"V\u00e1zquez","year":"2022","journal-title":"Physiol Meas"},{"issue":"1","key":"2025052920013574500_ocae108-B60","doi-asserted-by":"crossref","first-page":"16875","DOI":"10.1038\/s41598-023-43864-7","article-title":"Stochastic co-teaching for training neural networks with unknown levels of label noise","volume":"13","author":"de Vos","year":"2023","journal-title":"Sci Rep"},{"key":"2025052920013574500_ocae108-B61","doi-asserted-by":"crossref","first-page":"105750","DOI":"10.1016\/j.cmpb.2020.105750","article-title":"Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network","volume":"197","author":"Baghel","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"2025052920013574500_ocae108-B62","first-page":"1","author":"V\u00e1zquez","year":"2021"},{"key":"2025052920013574500_ocae108-B63","author":"Ding","year":"2022"},{"key":"2025052920013574500_ocae108-B64","doi-asserted-by":"crossref","first-page":"104425","DOI":"10.1016\/j.jbi.2023.104425","article-title":"Semi-Supervised Calibration of Noisy Event Risk (SCANER) with electronic health records","volume":"144","author":"Hong","year":"2023","journal-title":"J Biomed Inform"},{"key":"2025052920013574500_ocae108-B65","doi-asserted-by":"crossref","first-page":"1184744","DOI":"10.3389\/fgene.2023.1184744","article-title":"OCRFinder: a noise-tolerance machine learning method for accurately estimating open chromatin regions","volume":"14","author":"Ren","year":"2023","journal-title":"Front Genet"},{"key":"2025052920013574500_ocae108-B66","first-page":"477","author":"Tjandra","year":"2023"},{"key":"2025052920013574500_ocae108-B67","first-page":"765","author":"Vernekar","year":"2022"},{"key":"2025052920013574500_ocae108-B68","first-page":"567","author":"Xu","year":"2020"},{"issue":"1","key":"2025052920013574500_ocae108-B69","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13244-016-0534-1","article-title":"Error and discrepancy in radiology: inevitable or avoidable?","volume":"8","author":"Brady","year":"2017","journal-title":"Insights Imaging"},{"issue":"6","key":"2025052920013574500_ocae108-B70","doi-asserted-by":"crossref","first-page":"2809","DOI":"10.1364\/BOE.10.002809","article-title":"Automated stent coverage analysis in intravascular OCT (IVOCT) image volumes using a support vector machine and mesh growing","volume":"10","author":"Lu","year":"2019","journal-title":"Biomed Opt Express"},{"key":"2025052920013574500_ocae108-B71","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3389\/fmed.2020.00177","article-title":"Effects of label noise on deep learning-based skin cancer classification","volume":"7","author":"Hekler","year":"2020","journal-title":"Front Med (Lausanne)"},{"issue":"4","key":"2025052920013574500_ocae108-B72","doi-asserted-by":"crossref","first-page":"e5","DOI":"10.1016\/j.jaapos.2017.07.014","article-title":"Plus disease in ROP: why do experts disagree, and how can we improve diagnosis?","volume":"21","author":"Campbell","year":"2017","journal-title":"J Am Assoc Pediatr Ophthalmol Strabismus"},{"issue":"5","key":"2025052920013574500_ocae108-B73","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1038\/s41588-023-01372-4","article-title":"Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models","volume":"55","author":"Cosentino","year":"2023","journal-title":"Nat Genet"},{"issue":"19","key":"2025052920013574500_ocae108-B74","doi-asserted-by":"crossref","first-page":"7166","DOI":"10.3390\/s22197166","article-title":"Impact of label noise on the learning based models for a binary classification of physiological signal","volume":"22","author":"Ding","year":"2022","journal-title":"Sensors"},{"key":"2025052920013574500_ocae108-B75","first-page":"708","author":"Pechenizkiy","year":"2006"},{"issue":"1","key":"2025052920013574500_ocae108-B76","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1111\/aos.14895","article-title":"Detection of oedema on optical coherence tomography images using deep learning model trained on noisy clinical data","volume":"100","author":"Potapenko","year":"2022","journal-title":"Acta Ophthalmol"},{"key":"2025052920013574500_ocae108-B77","article-title":"728","author":"Khanal","year":"2023"},{"issue":"10","key":"2025052920013574500_ocae108-B78","doi-asserted-by":"crossref","first-page":"105002","DOI":"10.1088\/1361-6560\/ab82e8","article-title":"Generalization error analysis for deep convolutional neural network with transfer learning in breast cancer diagnosis","volume":"65","author":"Samala","year":"2020","journal-title":"Phys Med Biol"},{"issue":"9","key":"2025052920013574500_ocae108-B79","doi-asserted-by":"crossref","first-page":"3058","DOI":"10.3390\/jcm12093058","article-title":"Impact of noisy labels on dental deep learning\u2014calculus detection on bitewing radiographs","volume":"12","author":"B\u00fcttner","year":"2023","journal-title":"J Clin Med"},{"issue":"8","key":"2025052920013574500_ocae108-B80","doi-asserted-by":"crossref","first-page":"e18089","DOI":"10.2196\/18089","article-title":"Assessment of the robustness of convolutional neural networks in labeling noise by using chest X-ray images from multiple centers","volume":"8","author":"Jang","year":"2020","journal-title":"JMIR Med Inform"},{"issue":"7","key":"2025052920013574500_ocae108-B81","doi-asserted-by":"crossref","first-page":"100790","DOI":"10.1016\/j.patter.2023.100790","article-title":"The path toward equal performance in medical machine learning","volume":"4","author":"Petersen","year":"2023","journal-title":"Patterns"},{"issue":"3","key":"2025052920013574500_ocae108-B82","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TPAMI.2015.2456899","article-title":"Classification with noisy labels by importance reweighting","volume":"38","author":"Liu","year":"2015","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025052920013574500_ocae108-B83","first-page":"1944","author":"Patrini","year":"2017"},{"key":"2025052920013574500_ocae108-B84","first-page":"1","author":"Goldberger","year":"2016"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/31\/7\/1596\/58243631\/ocae108.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamia\/article-pdf\/31\/7\/1596\/58243631\/ocae108.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:01:51Z","timestamp":1748563311000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/31\/7\/1596\/7685298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,30]]},"references-count":84,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,5,30]]},"published-print":{"date-parts":[[2024,6,20]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocae108","relation":{},"ISSN":["1067-5027","1527-974X"],"issn-type":[{"value":"1067-5027","type":"print"},{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,7]]},"published":{"date-parts":[[2024,5,30]]}}}