Out-of-distribution detection for skin lesion images with deep isolation forest

Out-of-distribution detection for skin lesion images with deep isolation forest

Li, Xuan and Lu, Yuchen and Desrosiers, Christian and Liu, Xue

arXiv 2020

Abstract : In this paper, we study the problem of out-of-distribution detection in skin disease images. Publicly available medical datasets normally have a limited number of lesion classes (e.g. HAM10000 has 8 lesion classes). However, there exists a few thousands of clinically identified diseases. Hence, it is important if lesions not in the training data can be differentiated. Toward this goal, we propose DeepIF, a non-parametric Isolation Forest based approach combined with deep convolutional networks. We conduct comprehensive experiments to compare our DeepIF with three baseline models. Results demonstrate state-of-the-art performance of our proposed approach on the task of detecting abnormal skin lesions.