Explainable skin lesion diagnosis using taxonomies

Deep neural networks have rapidly become an indispensable tool in many classification applications. However, the inclusion of deep learning methods in medical diagnostic systems has come at the cost of diminishing their explainability. This significantly reduces the safety of a diagnostic system, since the physician is unable to interpret and validate the output. Therefore, in this work we aim to address this major limitation and improve the explainability of a skin cancer diagnostic system. We propose to lever-age two sources of information: (i) medical knowledge, in particular the taxonomic organization of skin lesions, which will be used to develop a hierarchical neural network; and (ii) recent advances in channel and spatial attention modules, which can identify interpretable features and regions in dermoscopy images. We demonstrate that the proposed approach achieves competitive results in two dermoscopy data sets (ISIC 2017 and 2018) and provides insightful information about its decisions, thus increasing the safety of the model. (c) 2020 Elsevier Ltd. All rights reserved.