Robust Selective Classification of Skin Lesions with Asymmetric Costs

Jacob Carse, Tamas Suveges, Stephen Hogg, Emanuele Trucco, Charlotte Proby, Colin Fleming, Stephen McKenna (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsCarole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan
Place of PublicationCham
PublisherSpringer
Pages112-121
Number of pages10
ISBN (Electronic)9783030877354
ISBN (Print)9783030877347
DOIs
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12959 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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