Retrieval-Guided Generation for Safer Histopathology Image Captioning
Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and factual inconsistency-serious issues in pathology. We investigate retrieval-guided generation (RGG) as a safer alternative, where captions are formed by summarizing expert text from visually similar cases rather than generated de novo. On the ARCH histopathology dataset, RGG improves semantic alignment with ground truth, achieving cosine similarity o
Record details
Published: 27 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (27 April 2026), “Retrieval-Guided Generation for Safer Histopathology Image Captioning,” evidence record 5289, https://ethics.ai/record/5289 (originally published by arXiv).
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