HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Department of Bioengineering and Imperial-X, Imperial College London（帝国理工学院伦敦校区生物工程系）；Department of Pathology, Xiangtan Maternal and Child Health Hospital（湘潭 maternal and child health hospital pathology department）；Department of Pathology, The First People’s Hospital of Xiangtan City（湘潭市第一人民医院病理科）
Pathology reports are structured, multi-granular documents encoding diagnostic conclusions, histological grades, and ancillary test results across one or more anatomical sites; yet existing pathology vision-language models (VLMs) reduce this output to a flat label or free-form text. We present HiPath, a lightweight VLM framework built on frozen UNI2 and Qwen3 backbones that treats structured report prediction as its primary training objective. Three trainable modules totalling 15M parameters address complementary aspects of the problem: a Hierarchical Patch Aggregator (HiPA) for multi-image visual encoding, Hierarchical Contrastive Learning (HiCL) for cross-modal alignment via optimal transport, and Slot-based Masked Diagnosis Prediction (Slot-MDP) for structured diagnosis generation. Trained on 749K real-world Chinese pathology cases from three hospitals, HiPath achieves 68.9% strict and 74.7% clinically acceptable accuracy with a 97.3% safety rate, outperforming all baselines under the same frozen backbone. Cross-hospital evaluation confirms generalisation with only a 3.4pp drop in strict accuracy while maintaining 97.1% safety.
Hierarchical Generalized Category Discovery for Brain Tumor Classification in Digital Pathology
Institute of Clinical Epidemiology, Public Health, Health Economics, Medical Statistics and Informatics（临床流行病学、公共卫生、健康经济学、医学统计与信息学研究所）；Institute of Neuropathology and Neuromolecular Pathology（神经病理学与神经分子病理学研究所）
Evidence-based diagnostic reasoning with multi-agent copilot for human pathology
Health Sciences and Technology, Harvard-MIT（哈佛-MIT健康科学与技术）；Department of Pathology, Massachusetts General Hospital, Harvard Medical School（麻省总医院病理科，哈佛医学院）；Cancer Program, Broad Institute of Harvard and MIT（哈佛-MIT博德研究所癌症项目）；Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）；Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)（麻省理工学院电气工程与计算机科学）；Harvard Data Science Initiative, Harvard University（哈佛大学数据科学计划）
Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patches because, for most occasions, only slide-level labels are available, and only a tiny region of the WSI is disease-positive area. However, WSI representation learning still remains an open problem due to: (1) patch sampling on a higher resolution may be incapable of depicting microenvironment information such as the relative position between the tumor cells and surrounding tissue…
Benchmarking Vision-Language Models for Automated Pathology Diagnosis and Report Generation
Ewha Womans University（梨花女子大学）；Korea University Anam Hospital（高丽大学安岩医院）；Korea University College of Medicine（高丽大学医学院）；Memorial Health Group（纪念健康集团）；Kameda Medical Center（龟田医疗中心）；Nagasaki University Graduate School of Biomedical Sciences（长崎大学生物医学科学研究生院）；Tata Memorial Hospital（塔塔纪念医院）；All India Institute Of Medical Sciences Delhi（全印医学科学研究所德里分院）；University Hospital Cologne, Medical Faculty, University of Cologne（科隆大学医院、科隆大学医学院）；Institute for Cancer Genetics and Informatics（癌症遗传学与信息学研究所）；Seoul National University（首尔大学）；Stony Brook University（石溪大学）；Yonsei University College of Medicine（延世大学医学院）；Tokyo Polytechnic University（东京工艺大学）；Nanyang Technological University（南洋理工大学）；Korea University（高丽大学）；Indiana University School of Medicine（印第安纳大学医学院）；Emory University（埃默里大学）；Shri Guru Gobind Singhji Institute of Engineering and Technology（斯里古鲁戈宾德辛格吉工程技术学院）；Viseur AI（维瑟人工智能公司）；EKFZ TU Dresden (KatherLab)（德累斯顿工业大学EKFZ（凯瑟实验室））；MTS Company（MTS公司）；University of Warwick（华威大学）；The Hong Kong University of Science and Technology（香港科技大学）；Harbin Institute of Technology（哈尔滨工业大学）
PathMem: Toward Cognition-Aligned Memory Transformation for Pathology MLLMs
University of Science and Technology of China（中国科学技术大学）；Shenzhen University（深圳大学）；Nanyang Technological University（南洋理工大学）；Imperial College London（伦敦帝国学院）；Huazhong University of Science and Technology（华中科技大学）
Agentic Visual Reasoning in Whole-Slide Pathology Images via Active Perception
College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）
Hepato-LLaVA: An Expert MLLM with Sparse Topo-Pack Attention for Hepatocellular Pathology Analysis on Whole Slide Images
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（人工智能学院，北京邮电大学）；Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（pathology department, 国家癌症中心/国家癌症临床研究中心/癌症医院, 中国医学科学院和北京协和医学院）
MLLM-HWSI: A Multimodal Large Language Model for Hierarchical Whole Slide Image Understanding
Department of Computer Science, Khalifa University of Science and Technology（卡利法科技大学计算机科学系）；Information Technology University（信息技术大学）；KAU（卡乌大学）；University of the Western Australia（西澳大学）
IBM Research Africa（IBM非洲研究院）；National Cancer Registry（国家癌症登记处）
Like most global cancer registries, the National Cancer Registry in South Africa employs expert human coders to label pathology reports using appropriate International Classification of Disease for Oncology (ICD-O) codes spanning 42 different cancer types. The annotation is extensive for the large volume of cancer pathology reports the registry receives annually from public and private sector institutions. This manual process, coupled with other challenges results in a significant 4-year lag in reporting of annual cancer statistics in South Africa. We present a hierarchical deep learning ensem…
SlideBank: A Persistent Hierarchical Evidence Bank for Consistent Whole-Slide Reasoning
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（矢量研究院）；Harvard University（哈佛大学）；Rice University（莱斯大学）；University of Chicago（芝加哥大学）；BC Cancer Agency（不列颠哥伦比亚癌症机构）
TaxoMIL: Taxonomy-Constrained Learning for Hierarchical Whole Slide Image Analysis
School of Electrical Engineering, Korea University（韩国大学电气工程学院）；Department of Hospital Pathology, The Catholic University of Korea College of Medicine（韩国天主教大学医学院医院病理学系）
IBM Research（IBM研究院）；ETH Zurich（苏黎世联邦理工学院）；EPFL（洛桑联邦理工学院）；IRCCS-Fondazione Pascale（帕斯卡尔IRCCS基金会）；ICAR-CNR（意大利国家研究委员会ICAR研究所）；Aurigen（Aurigen公司）；CHUV（洛桑大学医院）
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological entities is imperative for computer aided cancer patient care. To this end, several approaches have leveraged cell-graphs that encode cell morphology and organization to denote the tissue information. These allow for utilizing machine learning to map tissue representations to tissue functionality to help quantify their relationship. Though cellular in…
Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction from WSIs
Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University（华东师范大学上海市多维度信息处理重点实验室）；Department of General Surgery, Tongji Hospital, Tongji University School of Medicine（同济大学医学院附属同济医院普外科）
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；School of Software, Tsinghua University（清华大学软件学院）；School of Computer Science and Engineering, UNSW Sydney（新南威尔士大学计算机科学与工程学院）
Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosis
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属新华医院）；School of Artificial Intelligence（人工智能学院）；Department of Pathology（病理学部）；Department of Oral Pathology（口腔病理学部）；Department of Pediatric Hematology/Oncology（儿科血液肿瘤科）；Clinical Research and Innovation Unit（临床研究与创新单元）；Department of Pediatric Cardiology（儿童心脏病科）
McGill University（麦吉尔大学）；MILA (Québec Artificial Intelligence Institute)（MILA（魁北克人工智能研究所））；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
We propose a hierarchically structured variational inference model for accurately disentangling observable evidence of disease (e.g. brain lesions or atrophy) from subject-specific anatomy in brain MRIs. With flexible, partially autoregressive priors, our model (1) addresses the subtle and fine-grained dependencies that typically exist between anatomical and pathological generating factors of an MRI to ensure the clinical validity of generated samples; (2) preserves and disentangles finer pathological details pertaining to a patient's disease state. Additionally, we experiment with an alternat…
PathFL: Multi-Alignment Federated Learning for Pathology Image Segmentation
Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（新型一代人工智能技术及其交叉应用重点实验室（东南大学））；Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University（公共卫生学院生物统计学系，全球健康中心，南京医科大学）；Institute of High-Performance Computing, Agency for Science, Technology and Research（高性能计算研究所，科技研究局）
FOCUS: A Subspecialty Foundation Model for Intraoperative Central Nervous System Pathology
Beijing Neurosurgical Institute, Capital Medical University（首都医科大学附属北京神经外科研究所）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；The First Hospital of Hebei Medical University（河北医科大学第一医院）
ADPv2: A Hierarchical Histological Tissue Type-Annotated Dataset for Potential Biomarker Discovery of Colorectal Disease
Department of Computer Science \& Software Engineering, Concordia University, 2155 Guy St, Montreal, QC H3H 2L9, Canada；Department of Electrical \& Computer Engineering, University of Toronto, 10 King's College Rd, Toronto, ON M5S 3G8, Canada；Department of Chemistry \& Biology, Toronto Metropolitan University, 350 Victoria St. Toronto, ON M5B 2K3, Canada；Department of Medicine, Université de Montréal, Pavillon Roger-Gaudry, 2900 Edouard Montpetit Blvd, Montreal, QC H3T 1J4, Canada；Axe Cancer, Centre de recherche du CHUM, 900 Saint-Denis St, Montréal, QC H2X 0A9, Canada；Anatomic Pathology, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, ON M4N 3M5, Canada；Department of Laboratory Medicine \& Pathobiology, University of Toronto, Simcoe Hall, 1 King's College Circle, Toronto, ON M5S 3K3, Canada；Department of Pathology \& Molecular Medicine, Queen’s University, 88 Stuart Street Queen's University Kingston, ON K7L 3N6 Canada；Department of Pathology \& Molecular Medicine, Université de Montréal, 2900 Édouard-Montpetit Blvd, Montréal, QC H3T 1J4, Canada
Broad Institute（博德研究所）；University of Toronto（多伦多大学）
Vision Transformers (ViTs) and their multi-scale and hierarchical variations have been successful at capturing image representations but their use has been generally studied for low-resolution images (e.g. - 256x256, 384384). For gigapixel whole-slide imaging (WSI) in computational pathology, WSIs can be as large as 150000x150000 pixels at 20X magnification and exhibit a hierarchical structure of visual tokens across varying resolutions: from 16x16 images capture spatial patterns among cells, to 4096x4096 images characterizing interactions within the tissue microenvironment. We introduce a new…
Jarvis Research Center, Tencent YouTu Lab（腾讯优图实验室贾维斯研究中心）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Dept. of Computer Science, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）；AI Center, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院人工智能中心）；Signal Processing Laboratory (LTS4), EPFL, Lausanne, Switzerland（日内瓦联邦理工学院信号处理实验室）；University of Basel, Basel, Switzerland（巴塞尔大学）；University Hospital of Basel, Basel, Switzerland（巴塞尔大学医院）；Idiap Research Institute, Martigny, Switzerland（伊迪普研究 institute）；Icahn School of Medicine at Mount Sinai, New York, United States（辛辛那提医学中心伊坎医学院）
The College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education（教育部脑机智能技术重点实验室）
SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome
Wellcome Sanger Institute（沃森桑格研究所）；Cambridge Centre for AI in Medicine（剑桥人工智能医学中心）；Institute of AI for Health（人工智能与健康研究所）；Cambridge Stem Cell Institute（剑桥干细胞研究所）
SYNAPSE-Net: A Unified Framework with Lesion-Aware Hierarchical Gating for Robust Segmentation of Heterogeneous Brain Lesions
Department of Biomedical Engineering, Bangladesh University of Engineering and Technology（生物医学工程系，孟加拉国工程与技术大学）；Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology（计算机科学与工程系，孟加拉国工程与技术大学）；Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology（电气与电子工程系，孟加拉国工程与技术大学）
Single-Stage Hierarchical Rectification for Weakly Supervised Histopathology Segmentation
VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam（越南河内VinUniversity VinUni-Illinois智慧健康中心）；The Computer Vision and Medical AI Lab, VinUniversity, Hanoi, Vietnam（越南河内VinUniversity计算机视觉与医学人工智能实验室）；Posts and Telecommunications Institute of Technology, Hanoi, Vietnam（越南河内邮电技术学院）
School of Biomedical Engineering（生物医学工程学院）；Department of Pathology and Laboratory Medicine（病理与检验医学系）；Vancouver Prostate Centre（温哥华前列腺中心）；BC Cancer Institute（不列颠哥伦比亚省癌症研究所）；The University of British Columbia（不列颠哥伦比亚大学）
Anchor-driven Multi-modal Multi-scale Expert Selection for Survival Prediction
Nanjing University of Science and Technology（南京理工大学）；Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局）；Southeast University（东南大学）；Jiangnan University（江南大学）；Westlake University（西湖大学）
