SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT

2026-05-04 · Source: Computer Vision and Pattern Recognition · Field: Science & Research — Health & Medical Research, Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

The Structure-Aware Interpretable Learning (SAIL) framework enhances the explainability of deep learning models used for retinal disease diagnosis from Optical Coherence Tomography (OCT) images. Deep learning models, despite achieving expert-level accuracy in OCT analysis, face adoption challenges due to their "black box" nature. Existing post-hoc explainable AI (XAI) methods often fail to accurately delineate fine lesion structures, respect anatomical boundaries, or suppress noise. SAIL addresses these limitations by integrating retinal anatomical priors at the representation level and fusing them with semantic features. This approach, without modifying standard post-hoc XAI methods, generates sharper, more anatomically aligned, and clinically meaningful attribution maps. Experiments on various OCT datasets confirm that SAIL consistently improves interpretability, demonstrating that both structural priors and semantic features, along with their proper fusion, are crucial for high-quality explanations.

Key takeaway

For Computer Vision Engineers developing diagnostic tools with OCT, the SAIL framework offers a robust approach to enhance model explainability. By integrating anatomical priors and semantic features, you can achieve more clinically meaningful and trustworthy attribution maps, which is crucial for regulatory approval and clinical adoption. Consider adopting structure-aware representations in your deep learning pipelines to improve the interpretability of your medical imaging models.

Key insights

Integrating anatomical priors and semantic features improves deep learning explainability in medical imaging.

Principles

• Anatomical priors enhance XAI trustworthiness.
• Fusion of structural and semantic features is critical.

Method

SAIL integrates retinal anatomical priors into the representation layer and fuses them with semantic features, yielding sharper, anatomically aligned attribution maps for post-hoc XAI methods without modification.

In practice

• Apply SAIL to improve OCT diagnostic model interpretability.
• Combine structural and semantic data for better XAI.

Topics

• SAIL Framework
• Optical Coherence Tomography
• Explainable AI
• Retinal Disease Diagnosis
• Anatomical Priors

Best for: Computer Vision Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.