Uncertainty for safe utilization of machine learning in medical imaging : 6th international workshop, UNSURE 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. UNSURE (Workshop) (6th : 2024 : ...最新文献
{"title":"Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection.","authors":"Deeksha M Shama, Archana Venkataraman","doi":"10.1007/978-3-031-73158-7_1","DOIUrl":"10.1007/978-3-031-73158-7_1","url":null,"abstract":"<p><p>Supervised learning has become the dominant paradigm in computer-aided diagnosis. Generally, these methods assume that the training labels represent \"ground truth\" information about the target phenomena. In actuality, the labels, often derived from human annotations, are noisy/unreliable. This <i>aleoteric uncertainty</i> poses significant challenges for modalities such as electroencephalography (EEG), in which \"ground truth\" is difficult to ascertain without invasive experiments. In this paper, we propose a novel Bayesian framework to mitigate the effects of aleoteric label uncertainty in the context of supervised deep learning. Our target application is EEG-based epileptic seizure detection. Our framework, called BUNDL, leverages domain knowledge to design a posterior distribution for the (unknown) \"clean labels\" that automatically adjusts based on the data uncertainty. Crucially, BUNDL can be wrapped around any existing detection model and trained using a novel KL divergence-based loss function. We validate BUNDL on both a simulated EEG dataset and the Temple University Hospital (TUH) corpus using three state-of-the-art deep networks. In all cases, BUNDL improves seizure detection performance over existing noise mitigation strategies.</p>","PeriodicalId":520852,"journal":{"name":"Uncertainty for safe utilization of machine learning in medical imaging : 6th international workshop, UNSURE 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. UNSURE (Workshop) (6th : 2024 : ...","volume":"15167 ","pages":"3-13"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12107695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}