Abstract
In this paper, we study the problem of out-of-distribution (OOD) detection in skin lesion images. Publicly available medical datasets have a limited number of lesion classes compared to the number of possible diseases in real-life clinical applications. It is thus essential to develop methods that leverage available disease classes in existing datasets to detect previously-unseen types in an unsupervised manner. Toward this goal, we propose an unsupervised and non-parametric OOD detection approach, called DeepIF, which learns the normal distribution of features in a pre-trained CNN using Isolation Forests. We conduct comprehensive experiments on two different datasets and compare our DeepIF against four baseline models. Results demonstrate state-of-the-art performance of our proposed approach on the task of detecting unseen skin lesions.
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Li, X., Lu, Y., Desrosiers, C., Liu, X. (2020). Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_10
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DOI: https://doi.org/10.1007/978-3-030-59861-7_10
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