Pathology VQA

VQA: Visual Question Answering

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16 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

PlanPath: Exploration-Guided Orchestration for Patient-Level Pathology Reasoning
Oct 13, 2026Jiusong Ge, Mingzhao He, Qidong Liu +8Pathology VQA Medical VQA
Xi’an Jiaotong University · University of Cambridge · A*STAR +1
See, Measure, and Reason: Learning Visually Grounded Reasoning in Pathology
Sep 28, 2026Chengyang Zhang, Wenchuan Zhang, Bo Li +8Computational Pathology Medical VQA
College of Computer Science, Sichuan University · Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University · Department of Computer Science, School of Computing, National University of Singapore +1
Contamination, Prior, or Evidence? Decomposing and Training Evidence Use in Whole-Slide Vision-Language Models
Sep 26, 2026Wenhao Zhang, Zhongliang Zhou, Shiyuan Zhang +6Pathology VQA Medical VLMs
University of Virginia, Charlottesville · Merck & Co., Inc.
SlideBank: A Persistent Hierarchical Evidence Bank for Consistent Whole-Slide Reasoning
Aug 31, 2026Beidi Zhao, Gexin Huang, Ciro Zhang +6Memory-Augmented VLMs Medical VQA
University of British Columbia · Vector Institute · Harvard University +3
VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology
Aug 26, 2026Luca L. Weishaupt, Simone de Brot, Javier Asin +9VLM Evaluation Pathology VQA
Harvard-MIT HST · Mass General Brigham · Harvard Medical School +6
PathoArgus: Advancing Evidence-Grounded Long-Context Visual Reasoning across Gigapixel Whole-Slide and Multi-Slide Case Contexts
Aug 18, 2026Bowen Liu, Qixiang Zhang, Xiaomeng LiPathology VQA Medical VQA
The Hong Kong University of Science and Technology
Agentic Visual Reasoning in Whole-Slide Pathology Images via Active Perception
Aug 9, 2026Jingyun Chen, Fengchun Liu, Linghan Cai +5Active Perception Medical VQA
College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen · School of Computing and Data Science, The University of Hong Kong
Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning
Aug 6, 2026Bryan Wong, Xun Xu, Huazhu Fu +2Visual Question Answering Computational Pathology
1Korea Advanced Institute of Science and Technology (KAIST) · Institute of Advanced Intelligence and Computing (IAIC), A*STAR · Centre for Frontier AI Research (CFAR), A*STAR
PathView-Bench: Can Multimodal Large Language Models Achieve Fine-grained Multiscale Understanding of Pathology Images?
Jul 30, 2026Zongyi Chen, Yu Liang, Jie Lin +1Computational Pathology Medical VQA
National Institute for Data Science in Health and Medicine, Xiamen University · Institute of Artificial Intelligence Xiamen University, Xiamen, China · Department of Computer Science School of Informatics Xiamen University, Xiamen, China
PathScale-R1: Cross-scale Reasoning for Pathological Image Analysis
Jul 26, 2026Chi Phan, Tianyi Zhang, Yufeng Wu +7Computational Pathology Medical VQA
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117417 · Department of Biomedical Engineering, National University of Singapore, Singapore 117417, Singapore · Department of Pathology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China +1
Do Pathology Vision-Language Models Truly See Pathology?
Jul 23, 2026Chengyang Zhang, Wenchuan Zhang, Bo Li +10VLM Evaluation Computational Pathology
College of Computer Science, Sichuan University · Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University · Department of Computer Science, School of Computing, National University of Singapore +2
Enhancing Pathological VLMs with Cross-scale Reasoning
Jun 16, 2026Chi Phan, Tianyi Zhang, Qiaochu Xue +5Visual Question Answering Computational Pathology
Department of Electrical and Computer Engineering, National University of Singapore, Singapore · PuzzleLogic Pte Ltd, Singapore · Department of Pathology, Fujian Medical University Cancer Hospital & Fujian Cancer Hospital, Fuzhou, China
PathNavigate: A Training-Free Pathology Agent with Surprise-Guided Scan and Shared Slide Memory for Whole-Slide Image VQA
May 22, 2026Chunze Yang, Qidong Liu, Wenjie Zhao +10Healthcare Medical VQA
School of Comp. Science & Technology, Xi’an Jiaotong University · Tencent Jarvis Lab · University of Cambridge
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
May 5, 2026Carlijn Lems, Sander Moonemans, Natálie Klubíčková +53Computational Pathology Healthcare
Act Like a Pathologist: Tissue-Aware Whole Slide Image Reasoning
Feb 28, 2026Wentao Huang, Weimin Lyu, Peiliang Lou +8Pathology VQA Medical VQA
Stony Brook University, NY, USA · Mayo Clinic, MN, USA · Harvard Medical School, MA, USA +1
Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images
Feb 23, 2026Yuxuan Yang, Zhonghao Yan, Yi Zhang +6Pathology VQA Medical VLMs
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China · Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

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PlanPath: Exploration-Guided Orchestration for Patient-Level Pathology Reasoning

Jiusong Ge, Mingzhao He, Qidong Liu, Chunbao Wang+7

Xi’an Jiaotong University · University of Cambridge · A*STAR+1

Pathology VQA Medical VQA Whole-Slide Image Analysis

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A patient's pathology case spans tens of slides from distinct specimens and anatomical sites, each carrying non-redundant, site-specific evidence. Forming a patient-level conclusion therefore requires locating the slides relevant to a given question and integrating complementary findings across them. However, existing pathology agents analyze one slide at a time and describe morphology rather than measure it, leaving evidence acquisition across a patient's specimens and patient-level quantification underexplored. To address this gap, we propose PlanPath, an agentic framework for patient-level pathology visual question answering (VQA). PlanPath turns the question into an exploration plan that specifies the evidence required, routes to the slides likely to carry it, and drives coarse-to-fine acquisition from tissue architecture down to individual cells. Tissue- and cell-level tools supply the counts, proportions, and extents, which PlanPath aggregates across slides into a traceable answer. To enable evaluation, we build two patient-level VQA datasets for renal and gastric cancer, averaging 15 WSIs per patient, with pathologist-verified question-answer pairs derived from diagnostic reports. Experimental results show that PlanPath outperforms the strongest baseline by 9.4% and 12.1% in overall accuracy on the two datasets and further improves existing slide-level agents when plugged into our framework. The dataset and code are available at: https://github.com/JSGe-AI/PlanPath_1.