Applicability study of AI attribution methods for ophthalmic image classification.

ABSTRACT: Optical coherence tomography (OCT) enables early detection of vision-threatening diabetic retinopathy (DR) and retinal fluid accumulation, both major complications of diabetes. Despite high classification performance, deep learning models face limited clinical adoption due to poor interpretability. While attribution methods are effective for explaining predictions in the natural image domain, their applicability to medical imaging remains underexplored. To bridge this gap, our work explores how well these strong results transfer to the medical imaging domain. This study evaluates three cutting-edge attribution methods- DeepLIFT, AGI, and AttEXplore -for explaining predictions of a VGG16-based deep learning model in DR classification using widefield OCTA en face images and fluid detection i

SUBMITTER: Yavari A 

PROVIDER: S-EPMC12835177 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature