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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2111.04807 (eess)
[Submitted on 8 Nov 2021]

Title:Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection

Authors:Max Torop, Sandesh Ghimire, Wenqian Liu, Dana H. Brooks, Octavia Camps, Milind Rajadhyaksha, Jennifer Dy, Kivanc Kose
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Abstract: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.
Comments: NeurIPS: Medical Imaging Meets NeurIPS Workshop
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2111.04807 [eess.IV]
  (or arXiv:2111.04807v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2111.04807

Submission history

From: Max Torop [view email]
[v1] Mon, 8 Nov 2021 20:22:34 UTC (13 KB)
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