English(EN) PathKGen: Pathology Knowledge Assisted MLLM for Explainable Histopathology

PathKGen 使用知识图谱实现可解释的组织病理学报告

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了 PathKGen，这是一种新颖的多模态大语言模型 (MLLM)，旨在提高组织病理学报告的可解释性。PathKGen 将来自特定病理学知识图谱的外部结构化知识整合到其推理过程中，解决了当前仅依赖内部参数的 MLLM 的局限性。这种方法使模型能够生成更准确的报告，并提供视觉上直观的图表来说明其诊断推理，在 WSI-Bench 数据集上超越了先前最先进的方法。 AI

影响这项研究可能带来更值得信赖和可解释的医疗诊断人工智能工具，提高临床医生的信心和患者护理水平。

排序理由该集群包含一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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arXiv cs.CL TIER_1 English(EN) · Nadun Rajapaksha, Sandun Herath, Devnith Wijesinghe, Isiri Amani Withanawasam, Hasindri Watawana, Ranga Rodrigo · 2026-10-27 04:00

PathKGen：用于可解释组织病理学的病理知识辅助MLLM

arXiv:2610.33691v1 Announce Type: cross Abstract: Whole Slide Image (WSI) analysis serves as the fundamental method for the clinical diagnosis of cancer in digital histopathology. Recent advances in Multimodal Large Language Models (MLLMs) have enabled state-of-the-art (SOTA) aut…

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PathKGen：用于可解释组织病理学的病理知识辅助MLLM

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