Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification
Automated detection of vision impairing retina-based ocular conditions from fundus images is important for early screening, timely referral and reducing dependency on specialist-only assessment, for which neural network-based deep learning (DL) models have been widely utilized. However, explainability of the DL frameworks remains a major bottleneck for clinical adoption, particularly when model decisions are not linked to retinal regions that are clinically meaningful. To address this issue, thi
Record details
Published: 23 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Healthcare · Transparency
Retrieved: 25 July 2026
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ethics.ai (23 July 2026), “Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification,” evidence record 13501, https://ethics.ai/record/13501 (originally published by arXiv cs.LG).
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