ExplainabilityTransparencyAccountabilityeess.IV
Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation
Tillmann Rheude, Andreas Wirtz, Arjan Kuijper, Stefan Wesarg
September 30, 2024
Machine Learning for Biomedical Imaging
7 citations
Key Takeaways
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Limited explainability tools for medical AI: The paper identifies that while multiple explanation methods exist for classification tasks, only one CAM algorithm was available for segmentation tasks—a critical gap for medical imaging where understanding AI decisions is essential for clinical adoption.
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New method improves transparency in medical image analysis: The researchers developed "Seg-HiRes-Grad CAM," which creates visual heatmaps showing which parts of medical images the AI focuses on when making segmentation decisions, addressing explainability disadvantages in existing methods.
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Bridging the gap between different AI explanation approaches: The study demonstrates that techniques from classification-based AI explanation can be adapted and transferred to segmentation tasks, suggesting that advances in one area of AI interpretability can benefit other applications.
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Explainability matters for clinical trust: By focusing specifically on medical image segmentation, the paper underscores that healthcare applications of AI require robust explanation methods—doctors and patients need to understand why an AI system identified certain regions as abnormal or diseased before trusting its recommendations.
Abstract
Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understanding of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primarily revolves around understanding and interpreting this black-box behavior. One way of interpreting a CNN is the use of class activation maps (CAMs) that represent heatmaps to indicate the importance of image areas for the prediction of the CNN. For classification tasks, a variety of CAM algorithms exist. But for segmentation tasks, only one CAM algorithm for the interpretation of the output of a CNN exist. We propose a transfer between existing classification- and segmentation-based methods for more detailed, explainable, and consistent results which show salient pixels in semantic segmentation tasks. The resulting Seg-HiRes-Grad CAM is an extension of the segmentation-based Seg-Grad CAM with the transfer to the classification-based HiRes CAM. Our method improves the previously-mentioned existing segmentation-based method by adjusting it to recently published classification-based methods. Especially for medical image segmentation, this transfer solves existing explainability disadvantages.