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2021 | OriginalPaper | Buchkapitel

Improving the Explainability of Skin Cancer Diagnosis Using CBIR

verfasst von : Catarina Barata, Carlos Santiago

Erschienen in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

Verlag: Springer International Publishing

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Abstract

Explainability is a key feature for computer-aided diagnosis systems. This property not only helps doctors understand their decisions, but also allows less experienced practitioners to improve their knowledge. Skin cancer diagnosis is a field where explainability is of critical importance, as lesions of different classes often exhibit confounding characteristics. This work proposes a deep neural network (DNN) for skin cancer diagnosis that provides explainability through content-based image retrieval. We explore several state-of-the-art approaches to improve the feature space learned by the DNN, namely contrastive, distillation, and triplet losses. We demonstrate that the combination of these regularization losses with the categorical cross-entropy leads to the best performances on melanoma classification, and results in a hybrid DNN that simultaneously: i) classifies the images; and ii) retrieves similar images justifying the diagnosis. The code is available at https://github.com/catarina-barata/CBIR_Explainability_Skin_Cancer.

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Vorheriges Kapitel Sharpening Local Interpretable Model-Agnostic Explanations for Histopathology: Improved Understandability and Reliability

Nächstes Kapitel PAC Bayesian Performance Guarantees for Deep (Stochastic) Networks in Medical Imaging

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https://challenge.isic-archive.com/.

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Titel: Improving the Explainability of Skin Cancer Diagnosis Using CBIR
verfasst von: Catarina Barata
Carlos Santiago
Verlag: Springer International Publishing
Buch: Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Print ISBN: 978-3-030-87198-7

Electronic ISBN: 978-3-030-87199-4

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-87199-4_52

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