Clinically Explainable Disease Diagnosis Based on Biomarker Activation Map

作者：Pengxiao Zang, Carol A. Wang, Tristan T. Hormel, Steven T. Bailey, Thomas S. Hwang, Yali Jia · 发表于：IEEE Transactions on Biomedical Engineering · 年份：2025 · DOI：10.1109/tbme.2025.3614518 · 被引用次数：1 · 研究领域：Retinal Imaging and Analysis、AI in cancer detection、Explainable Artificial Intelligence (XAI)

OBJECTIVE: Artificial intelligence (AI)-based disease classifiers have achieved specialist-level performances in several diagnostic tasks. However, real-world adoption of these classifiers remains challenging due to the black box issue. Here, we report a novel biomarker activation map (BAM) generation framework that can provide clinically meaningful explainability to current AI-based disease classifiers. METHODS: We designed the framework based on the concept of residual counterfactual explanation by generating counterfactual outputs that could reverse the decision-making of the disease classifier. The BAM was generated as the difference map between the counterfactual output and original input with postprocessing. We evaluated the BAM on four different disease classifiers, including an age-related macular degeneration classier based on fundus photography, a diabetic retinopathy classifier based on optical coherence tomography angiography, a brain tumor classifier based on magnetic resonance imaging (MRI), and a breast cancer classifier based on computerized tomography (CT) scans. RESULTS: The highlighted regions in the BAM correlated highly with manually demarcated biomarkers of each disease. CONCLUSION/SIGNIFICANCE: The BAM can improve the clinical applicability of an AI-based disease classifier by providing intuitive output clinicians can use to understand and verify the diagnostic decision.