Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis

• URL: http://arxiv.org/abs/2004.01610v1
• Date: Fri, 3 Apr 2020 14:59:08 GMT
• Title: Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis
• Authors: David Major, Dimitrios Lenis, Maria Wimmer, Gert Sluiter, Astrid Berg, and Katja B\"uhler
• Abstract summary: We present a model saliency mapping framework tailored to medical imaging. We replace techniques with a strong neighborhood conditioned inpainting approach, which avoids implausible artefacts.
• Score: 2.512212190779389
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical applicability of automated decision support systems depends on a robust, well-understood classification interpretation. Artificial neural networks while achieving class-leading scores fall short in this regard. Therefore, numerous approaches have been proposed that map a salient region of an image to a diagnostic classification. Utilizing heuristic methodology, like blurring and noise, they tend to produce diffuse, sometimes misleading results, hindering their general adoption. In this work we overcome these issues by presenting a model agnostic saliency mapping framework tailored to medical imaging. We replace heuristic techniques with a strong neighborhood conditioned inpainting approach, which avoids anatomically implausible artefacts. We formulate saliency attribution as a map-quality optimization task, enforcing constrained and focused attributions. Experiments on public mammography data show quantitatively and qualitatively more precise localization and clearer conveying results than existing state-of-the-art methods.

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