Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 39 of its 77 references that are in the map. First posted on arXiv 2 Mar 2021.
Builds on
39 of 77 references.
References · 39
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
- 2013Intriguing properties of neural networkscited by 5,114
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2015Learning Deep Features for Discriminative Localizationcited by 2,430
- 2013Visualizing and Understanding Convolutional Networkscited by 2,278
- 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
- 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
27 more
- 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
- 2018Sanity Checks for Saliency Mapscited by 1,149
- 2019Unmasking Clever Hans Predictors and Assessing What Machines Really Learncited by 359
- 2017Methods for Interpreting and Understanding Deep Neural Networkscited by 342
- 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
- 2020Concept Whitening for Interpretable Image Recognitioncited by 225
- 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
- 2019Interpretations are useful: penalizing explanations to align neural networks with prior knowledgecited by 144
- 2018Explaining Explanations in AIcited by 136
- 2020CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentationcited by 79
- 2020A Disentangling Invertible Interpretation Network for Explaining Latent Representationscited by 57
- 2017MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Networkcited by 56
- 2020On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectorscited by 39
- 2017Medical Image Retrieval using Deep Convolutional Neural Networkcited by 33
- 2019Resolving challenges in deep learning-based analyses of histopathological images using explanation methodscited by 32
- 2019(De)Constructing Bias on Skin Lesion Datasetscited by 30
- 2019Deep neural network or dermatologist?cited by 20
- 2020Explaining AI-based Decision Support Systems using Concept Localization Mapscited by 16
- 2020Illumination-based Transformations Improve Skin Lesion Segmentation in Dermoscopic Imagescited by 10
- 2020Architecture Disentanglement for Deep Neural Networkscited by 10
- 2017Fully Convolutional Neural Networks to Detect Clinical Dermoscopic Featurescited by 9
- 2018Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classificationcited by 9
- 2020The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Reportcited by 6
- 2018Collaborative Human-AI (CHAI): Evidence-Based Interpretable Melanoma Classification in Dermoscopic Imagescited by 4
- 2019Interpretable Classification from Skin Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Studycited by 3
- 2018What evidence does deep learning model use to classify Skin Lesions?cited by 2
- 2017Skin Lesion Classification using Class Activation Mapcited by 1
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