Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis

Abstract

Early detection of skin cancers like melanoma is crucial to ensure high chances of survival for patients. Clinical application of Deep Learning (DL)-based Decision Support Systems (DSS) for skin cancer screening has the potential to improve the quality of patient care. The majority of work in the medical AI community focuses on a diagnosis setting that is mainly relevant for autonomous operation. Practical decision support should, however, go beyond plain diagnosis and provide explanations. This paper provides an overview of works towards explainable, DL-based decision support in medical applications with the example of skin cancer diagnosis from clinical, dermoscopic and histopathologic images. Analysis reveals that comparably little attention is payed to the explanation of histopathologic skin images and that current work is dominated by visual relevance maps as well as dermoscopic feature identification. We conclude that future work should focus on meeting the stakeholder's cognitive concepts, providing exhaustive explanations that combine global and local approaches and leverage diverse modalities. Moreover, the possibility to intervene and guide models in case of misbehaviour is identified as a major step towards successful deployment of AI as DL-based DSS and beyond.

References in corpus (20)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Methods for Interpreting and Understanding Deep Neural Networks
• Striving for Simplicity: The All Convolutional Net
• Explaining Explanations in AI
• Multiple Object Recognition with Visual Attention
• Sanity Checks for Saliency Maps
• Not Just a Black Box: Learning Important Features Through Propagating Activation Differences
• Medical Image Retrieval using Deep Convolutional Neural Network
• Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
• Concept Whitening for Interpretable Image Recognition
• One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
• Deep neural network or dermatologist?
• On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
• Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
• Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study
• Skin Lesion Classification using Class Activation Map
• What evidence does deep learning model use to classify Skin Lesions?
• Human-interpretable model explainability on high-dimensional data
• Patchnet: Interpretable Neural Networks for Image Classification
• The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report