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Robust Selective Classification of Skin Lesions with Asymmetric Costs

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 12959)

Abstract

Automated image analysis of skin lesions has potential to improve diagnostic decision making. A clinically useful system should be selective, rejecting images it is ill-equipped to classify, for example because they are of lesion types not represented well in training data. Furthermore, lesion classifiers should support cost-sensitive decision making. We investigate methods for selective, cost-sensitive classification of lesions as benign or malignant using test images of lesion types represented and not represented in training data. We propose EC-SelectiveNet, a modification to SelectiveNet that discards the selection head at test time, making decisions based on expected costs instead. Experiments show that training for full coverage is beneficial even when operating at lower coverage, and that EC-SelectiveNet outperforms standard cross-entropy training, whether or not temperature scaling or Monte Carlo dropout averaging are used, in both symmetric and asymmetric cost settings.

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Notes

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    GitHub Repository: https://github.com/UoD-CVIP/Selective_Dermatology.

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Acknowledgments

This paper reports independent research funded by the National Institute for Health Research (Artificial Intelligence, Deep learning for effective triaging of skin disease in the NHS, AI_AWARD01901) and NHSX. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research, NHSX or the Department of Health and Social Care. This research was also funded by the Detect Cancer Early programme, and the Discovery Institute of Dermatology.

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Correspondence to Stephen McKenna .

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Carse, J. et al. (2021). Robust Selective Classification of Skin Lesions with Asymmetric Costs. In: , et al. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. UNSURE PIPPI 2021 2021. Lecture Notes in Computer Science(), vol 12959. Springer, Cham. https://doi.org/10.1007/978-3-030-87735-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-87735-4_11

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