PathReportEval: A Systematic Benchmark for Pathology Report Generation
PathReportEval is a standardized benchmark and evaluation framework for pathology report generation from whole-slide images. It evaluates four methods on three datasets (TCGA, HistAI, REG 2025) using three pathology foundation encoders. The key contribution is the Clinical Report Quality Score (CRQS), which measures factual correctness across four dimensions: clinical fact coverage, key information recall, hallucination rate, and clinical discordance. Experiments show traditional metrics like BLEU and ROUGE are weakly correlated with clinical accuracy, while CRQS reveals meaningful differences.
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[Submitted on 20 Jul 2026]
Title:PathReportEval: A Systematic Benchmark for Pathology Report Generation
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Abstract:Pathology report generation from whole-slide images (WSIs) is a rapidly growing multimodal learning problem, yet progress is difficult to measure because existing studies use heterogeneous datasets, model settings, visual encoders, and evaluation protocols. Moreover, commonly used natural language generation metrics, including BLEU, ROUGE, and METEOR, primarily reward lexical similarity and often fail to detect clinically consequential errors such as omitted diagnoses, hallucinated findings, or discordant tumor attributes.
We present a standardized benchmark and evaluation framework for pathology report generation. The benchmark evaluates four representative methods across three datasets (TCGA, HistAI, and REG 2025) using three pathology foundation encoders (CONCHv1.5, UNI2-h, and H-Optimus-1). Our framework standardizes preprocessing, feature extraction, training, decoding, and evaluation, enabling fair comparison across models while providing a modular platform for integrating new methods, datasets, and encoders.
A central contribution is the Clinical Report Quality Score (CRQS), a clinically grounded metric for evaluating factual correctness. CRQS maps reference and generated reports into structured clinical attributes and measures four complementary dimensions: clinical fact coverage, key information recall, hallucination rate, and clinical discordance, producing both an overall score and interpretable sub-scores.
Experiments demonstrate that conventional language-generation metrics are weakly aligned with clinical correctness and frequently overestimate report quality. In contrast, CRQS reveals clinically meaningful differences between models and encoders that lexical metrics fail to capture. Together, the benchmark, public plug-and-play framework, and CRQS establish a reproducible foundation for rigorous evaluation of pathology report generation.
Subjects:
Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.18448 [cs.CL]
(or arXiv:2607.18448v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.18448
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Suryakant Singh [view email] [v1] Mon, 20 Jul 2026 18:59:47 UTC (1,022 KB)
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