‹ back to 2026-07-25

Do Pathology Vision-Language Models Truly See Pathology?

Research Medical/Healthcare AI

Ranking

Overall 76

Content 90

Popularity 42

Observed public metrics from 1 member.

Merged summary

TL;DR - PathBind is a 2,600-sample benchmark testing whether pathology vision-language models genuinely connect answers to visual evidence. Results reveal a substantial gap between VQA accuracy and visual-semantic grounding.

Gemini-3-Pro averaged 53.5% across five pathology VQA benchmarks without visual input, exposing textual shortcuts.
Domain training improved accuracy without proportional visual binding gains; Patho-R1-7B underperformed Qwen2.5-VL-7B on multimodal gain and attention IoU.
Entity-level attention was diffuse and weakly query-specific on PathVG.
Evaluations covered 18 VLMs on VQA and 10 on region-level grounding.

Sources (1)

Do Pathology Vision-Language Models Truly See Pathology?

arXiv cs.CV Chengyang Zhang, Wenchuan Zhang, Bo Li, Xinyu Liu, Jiaming Yang, Mengran Li, Chenxun Deng, Jie Chen, Yang Zhang, Wei Ju, Yuhao Yi, Hong Bu, Jiancheng Lv 2026-07-23 arXiv:2607.21065

Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0

Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-07 09:45:03.626028 UTC

Gemini-3-Pro averaged 53.5% across five pathology VQA benchmarks without visual input, exposing textual shortcuts.
Domain training improved accuracy without proportional visual binding gains; Patho-R1-7B underperformed Qwen2.5-VL-7B on multimodal gain and attention IoU.
Entity-level attention was diffuse and weakly query-specific on PathVG.
Evaluations covered 18 VLMs on VQA and 10 on region-level grounding.

item →