Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection

Dana H. Brooks; Jennifer Dy; Kivanc Kose; Max Torop; Milind Rajadhyaksha; Octavia Camps; Sandesh Ghimire; Wenqian Liu

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Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection

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arxiv 2111.04807 v1 pith:4FWDFKDR submitted 2021-11-08 eess.IV cs.CVcs.LG

Max Torop , Sandesh Ghimire , Wenqian Liu , Dana H. Brooks , Octavia Camps , Milind Rajadhyaksha , Jennifer Dy , Kivanc Kose This is my paper · ORCID

classification eess.IVcs.CVcs.LG

keywords unsupervisedappliedapproachesdatadetectionmedicalout-of-distributionrecent

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There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data. Here, we present preliminary findings of our unsupervised OOD detection algorithm, SimCLR-LOF, as well as a recent state of the art approach (SSD), applied on medical images. SimCLR-LOF learns semantically meaningful features using SimCLR and uses LOF for scoring if a test sample is OOD. We evaluated on the multi-source International Skin Imaging Collaboration (ISIC) 2019 dataset, and show results that are competitive with SSD as well as with recent supervised approaches applied on the same data.

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