HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction
Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet existing pathology vision-language models (VLMs) reduce this output to a flat label or free-form text. We present HiPath, a lightweight VLM framework built on frozen UNI2 and Qwen3 backbones that treats structured report prediction as its primary training objective. Three trainable modules totalling 15M parameters add
Record details
Published: 20 March 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026
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ethics.ai (20 March 2026), “HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction,” evidence record 6953, https://ethics.ai/record/6953 (originally published by arXiv).
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