Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning

• URL: http://arxiv.org/abs/2105.11160v2
• Date: Tue, 25 May 2021 11:53:15 GMT
• Title: Out-of-Distribution Detection in Dermatology using Input Perturbation and Subset Scanning
• Authors: Hannah Kim, Girmaw Abebe Tadesse, Celia Cintas, Skyler Speakman, Kush Varshney
• Abstract summary: Current skin disease models could make incorrect inferences for test samples from different hardware devices or clinical settings. We propose a simple yet effective approach that detect these OOD samples prior to making any decision.
• Score: 5.674998177844528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in deep learning have led to breakthroughs in the development of automated skin disease classification. As we observe an increasing interest in these models in the dermatology space, it is crucial to address aspects such as the robustness towards input data distribution shifts. Current skin disease models could make incorrect inferences for test samples from different hardware devices and clinical settings or unknown disease samples, which are out-of-distribution (OOD) from the training samples. To this end, we propose a simple yet effective approach that detect these OOD samples prior to making any decision. The detection is performed via scanning in the latent space representation (e.g., activations of the inner layers of any pre-trained skin disease classifier). The input samples could also perturbed to maximise divergence of OOD samples. We validate our ODD detection approach in two use cases: 1) identify samples collected from different protocols, and 2) detect samples from unknown disease classes. Additionally, we evaluate the performance of the proposed approach and compare it with other state-of-the-art methods. Furthermore, data-driven dermatology applications may deepen the disparity in clinical care across racial and ethnic groups since most datasets are reported to suffer from bias in skin tone distribution. Therefore, we also evaluate the fairness of these OOD detection methods across different skin tones. Our experiments resulted in competitive performance across multiple datasets in detecting OOD samples, which could be used (in the future) to design more effective transfer learning techniques prior to inferring on these samples.

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