ArticleIEEE transactions on bio-medical engineering2026
Clinically Explainable Disease Diagnosis Based on Biomarker Activation Map.
Article in IEEE transactions on bio-medical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Will AI Replace Physicians in the Near Future? AI Adoption Barriers in Medicine.Diagnostics (Basel, Switzerland) · 2026Review
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6 authors.
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Abstract
objectiveArtificial 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.
methodsWe 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.
resultsThe 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.
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