{
  "id": 13501,
  "url": "https://arxiv.org/abs/2607.21068v1",
  "title": "Counterfactual Explainability Framework With CycleGAN And Counterfactual-Classifier Alignnment Score for Retinal Disease Classification",
  "summary": "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",
  "authors": "Kritanu Chattopadhyay, Sayanjit Singha Roy, Soumya Chatterjee",
  "category": "research",
  "topics": "healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-23T09:00:31.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "arxiv-cslg",
  "source_name": "arXiv cs.LG",
  "source_homepage": "https://arxiv.org/list/cs.LG/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13501",
  "original_url": "https://arxiv.org/abs/2607.21068v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}