{
  "id": 17984,
  "url": "https://arxiv.org/abs/2603.18846",
  "title": "Towards Interpretable Foundation Models for Retinal Fundus Images",
  "summary": "Foundation models are used to extract transferable representations from large amounts of unlabeled data, typically via self-supervised learning (SSL). However, many of these models rely on architectures that offer limited interpretability, a critical issue in high-stakes domains such as medical imaging. We propose DualIFM, a foundation model that is interpretable-by-design via a BagNet backbone whose small receptive fields generate class evidence maps that are faithful to the model's decision-ma",
  "authors": "Samuel Ofosu Mensah, Camila Roa, Kerol Djoumessi, Philipp Berens",
  "category": "research",
  "topics": "safety-alignment,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-03T20:00:00.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/17984",
  "original_url": "https://arxiv.org/abs/2603.18846",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}