← 研踪 PaperTrailarXiv:2611.15325 · v1
CaseScope: A Multi-Field Benchmark for Case-Level Pathology Diagnosis with Multimodal LLMs
首次提交 2026-11-11 19:04
版本更新 2026-11-11 19:04
雷达观测 2026-11-13 11:11
摘要
Pathologists rarely diagnose from a single image: they scan a section at low power for architecture, zoom into selected fields for cytology, and integrate the evidence across views and scales. Pathology benchmarks for multimodal large language models (MLLMs), however, mostly ask about one image or require whole-slide pipelines, and the few that show several fields draw nested regions from cancer slides of one public cohort. We introduce CaseScope, a benchmark of diagnosis from several curated fields of one lesion: CaseScope-Teaching, 436 cases from a residency-training image library, reconstructed, de-identified, harmonised against a WHO-based ontology and re-examined by a pathologist wherever an automated pre-review raised a doubt, and CaseScope-Atlas, 800 cases from 88 published atlases (387 of them figure series of one disease), labelled from the captions by two LLMs that must agree. They cover 843 entities across 18 organ systems and pose 4,679 items in six tasks, from open-ended and ten-way diagnosis with ontology-guided hard negatives under circular evaluation to the verification and location of morphological findings. Across twenty-eight open and four proprietary MLLMs, diagnoses are unstable -- the best open and proprietary models are correct under all ten option rotations on 17.9% and 61.7% of the teaching cases -- and wrong answers favour the classic look-alike; open models make little use of the additional fields on teaching cases, where the one-line specimen site matters more, although on atlas cases with three or more fields the fields count about as much as the site; and most open models verify findings barely above chance (median 62.0% vs. 50%, best 81.5%), gaining a median +3.4 points from images; proprietary models reach 83.2-93.2%. Rankings broadly replicate between the sources. The evaluation code is available at https://github.com/Wenchuan-Zhang/CaseScope.
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CaseScope: A Multi-Field Benchmark for Case-Level Pathology Diagnosis with Multimodal LLMs
Pathologists rarely diagnose from a single image: they scan a section at low power for architecture, zoom into selected fields for cytology, and integrate the evidence across views and scales. Pathology benchmarks for multimodal large language models (MLLMs), however, mostly ask about one image or require whole-slide pipelines, and the few that show several fields draw nested regions from cancer slides of one public cohort. We introduce CaseScope, a benchmark of diagnosis from several curated fields of one lesion: CaseScope-Teaching, 436 cases from a residency-training image library, reconstructed, de-identified, harmonised against a WHO-based ontology and re-examined by a pathologist wherever an automated pre-review raised a doubt, and CaseScope-Atlas, 800 cases from 88 published atlases (387 of them figure series of one disease), labelled from the captions by two LLMs that must agree. They cover 843 entities across 18 organ systems and pose 4,679 items in six tasks, from open-ended and ten-way diagnosis with ontology-guided hard negatives under circular evaluation to the verification and location of morphological findings. Across twenty-eight open and four proprietary MLLMs, diagnoses are unstable -- the best open and proprietary models are correct under all ten option rotations on 17.9% and 61.7% of the teaching cases -- and wrong answers favour the classic look-alike; open models make little use of the additional fields on teaching cases, where the one-line specimen site matters more, although on atlas cases with three or more fields the fields count about as much as the site; and most open models verify findings barely above chance (median 62.0% vs. 50%, best 81.5%), gaining a median +3.4 points from images; proprietary models reach 83.2-93.2%. Rankings broadly replicate between the sources. The evaluation code is available at https://github.com/Wenchuan-Zhang/CaseScope.
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