Unsupervised representation learning for detecting out of distribution samples in dermoscopy images of eight types of skin lesions

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

doi:10.1117/12.2609885

7 March 2022 Unsupervised representation learning for detecting out of distribution samples in dermoscopy images of eight types of skin lesions

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

Author Affiliations +

Proceedings Volume PC11934, Photonics in Dermatology and Plastic Surgery 2022; PC119340H (2022) https://doi.org/10.1117/12.2609885
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

We present the initial results of two unsupervised out-of-distribution (OOD) detection algorithms, designed to flag dermoscopic images of lesions from classes not seen during training. When evaluated on the ISIC 2019 dataset - using 6 classes as in-distribution and 2 as OOD - the scores from our algorithms produced AUROC’s of 0.694/0.642. The images in ISIC 2019 mainly come from two datasets - HAM and BCN. When restricting our evaluation to consider only images from HAM the AUROC was 0.758/0.765, and when considering the images from BCN only, the AUROC dropped to 0.645/0.504.

Conference Presentation

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Max Torop, Wenqian Liu, Dana H. Brooks, Milind Rajadhyaksha, Jennifer G. Dy, Octavia Camps, Sandesh Ghimire, and Kivanc Kose "Unsupervised representation learning for detecting out of distribution samples in dermoscopy images of eight types of skin lesions", Proc. SPIE PC11934, Photonics in Dermatology and Plastic Surgery 2022, PC119340H (7 March 2022); https://doi.org/10.1117/12.2609885

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