{
  "id": 3969,
  "url": "https://arxiv.org/abs/2605.21186v1",
  "title": "SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection",
  "summary": "Interpretability in object detection provides crucial confidence support for clinical auxiliary diagnosis. However, in tiny bacteria detection, traditional explanation methods often suffer from blurred foreground boundaries and diffuse feature attribution due to the extreme sparsity of target morphological features and severe interference from complex backgrounds. Such limitations hinder the provision of logically coherent morphological evidence. To bridge this gap, we propose a novel eXplainabl",
  "authors": "Wanying Tan, Shuo Yan, Dazhi Huang, Yazheng Liu, Zili Shao, Rufeng Chen et al.",
  "category": "research",
  "topics": "safety-alignment,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-20T13:51:22.000Z",
  "fetched_at": "2026-07-14T16:30:41.577Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3969",
  "original_url": "https://arxiv.org/abs/2605.21186v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}