DALPHIN benchmark finds the pathology-specific AI copilot PathChat+ shows no statistically significant difference from expert pathologists in 4 of 6 tasks, with general models matching in 1-2 tasks, on a diverse open dataset released for ongoing evaluation.
A versatile pathology co-pilot via reasoning enhanced multimodal large language model
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A new open pipeline and dataset enable training of a vision-language model for whole-slide pathology VQA that outperforms MedGemma on tissue identification, neoplasm detection, and differential diagnosis.
Cross-scale supervision from a leakage-curated multi-magnification VQA benchmark improves pathology VLMs on both multi-image and single-image evaluation.
PathPocket constructs a 4.55M-entity pathology hypergraph from 110k graded documents and deploys a multi-agent framework that outperforms prior systems on 200k cases while raising pathologist accuracy in user studies.
EndoGov uses specialist agents plus a governance layer with hard and soft rule paths to deliver guideline-compliant endometrial cancer risk stratification, reporting 0.943 accuracy and 0.93% logic-violation rate on TCGA-UCEC while outperforming neural baselines on CPTAC-UCEC.
PathoSage is a three-stage framework using Structured Evidence Deliberation and a Beta-Bernoulli experience system to improve patch-level pathology reasoning by mitigating hallucinations and tool conflicts.
citing papers explorer
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DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
DALPHIN benchmark finds the pathology-specific AI copilot PathChat+ shows no statistically significant difference from expert pathologists in 4 of 6 tasks, with general models matching in 1-2 tasks, on a diverse open dataset released for ongoing evaluation.
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Democratising Pathology Co-Pilots: An Open Pipeline and Dataset for Whole-Slide Vision-Language Modelling
A new open pipeline and dataset enable training of a vision-language model for whole-slide pathology VQA that outperforms MedGemma on tissue identification, neoplasm detection, and differential diagnosis.
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Enhancing Pathological VLMs with Cross-scale Reasoning
Cross-scale supervision from a leakage-curated multi-magnification VQA benchmark improves pathology VLMs on both multi-image and single-image evaluation.
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A Multi-modal Agentic Co-pilot for Evidence Grounded Computational Pathology
PathPocket constructs a 4.55M-entity pathology hypergraph from 110k graded documents and deploys a multi-agent framework that outperforms prior systems on 200k cases while raising pathologist accuracy in user studies.
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EndoGov: A knowledge-governed multi-agent expert system for endometrial cancer risk stratification
EndoGov uses specialist agents plus a governance layer with hard and soft rule paths to deliver guideline-compliant endometrial cancer risk stratification, reporting 0.943 accuracy and 0.93% logic-violation rate on TCGA-UCEC while outperforming neural baselines on CPTAC-UCEC.
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PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
PathoSage is a three-stage framework using Structured Evidence Deliberation and a Beta-Bernoulli experience system to improve patch-level pathology reasoning by mitigating hallucinations and tool conflicts.