Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Department of Computer Science（计算机科学系）；ARIC；Khalifa University of Science and Technology（科技大学）；Information Technology University of the Punjab（旁遮普信息科技大学）；University of the Western Australia（西澳大学）
Shahad Albastaki, Anabia Sohail, Iyyakutti Iyappan Ganapathi, Basit Alawode, Asim Khan, Sajid Javed, Naoufel Werghi, Mohammed Bennamoun, Arif Mahmood
In Computational Pathology (CPath), the introduction of Vision-Language Models (VLMs) has opened new avenues for research, focusing primarily on aligning image-text pairs at a single magnification level. However, this approach might not be sufficient for tasks like cancer subtype classification, tissue phenotyping, and survival analysis due to the limited level of detail that a single-resolution image can provide. Addressing this, we propose a novel multi-resolution paradigm leveraging Whole Slide Images (WSIs) to extract histology patches at multiple resolutions and generate corresponding textual descriptions through advanced CPath VLM. We introduce visual-textual alignment at multiple resolutions as well as cross-resolution alignment to establish more effective text-guided visual representations. Cross-resolution alignment using a multimodal encoder enhances the model's ability to capture context from multiple resolutions in histology images. Our model aims to capture a broader range of information, supported by novel loss functions, enriches feature representation, improves discriminative ability, and enhances generalization across different resolutions. Pre-trained on a comprehensive TCGA dataset with 34 million image-language pairs at various resolutions, our fine-tuned model outperforms state-of-the-art (SOTA) counterparts across multiple datasets and tasks, demonstrating its effectiveness in CPath. The code is available on GitHub at: https://github.com/BasitAlawode/MR-PLIP
PathReasoner-R1: Instilling Structured Reasoning into Pathology Vision-Language Model via Knowledge-Guided Policy Optimization
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Microsoft Research（微软研究院）；National University of Singapore（新加坡国立大学）；Technical University of Dresden（德累斯顿技术大学）
Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Arizona State University（亚利桑那州立大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Massachusetts Institute of Technology（麻省理工学院）
Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Pathology Analysis
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院第一附属医院）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Institute for AI in Medicine, University Hospital Essen（埃森大学医院人工智能医学研究所）；University of Duisburg-Essen（杜伊斯堡-埃森大学）；German Cancer Consortium (DKTK)（德国癌症联盟）
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Weill Cornell Medical College（威尔康奈尔医学院）
Before the recent success of deep learning methods for automated medical image analysis, practitioners used handcrafted radiomic features to quantitatively describe local patches of medical images. However, extracting discriminative radiomic features relies on accurate pathology localization, which is difficult to acquire in real-world settings. Despite advances in disease classification and localization from chest X-rays, many approaches fail to incorporate clinically-informed domain knowledge. For these reasons, we propose a Radiomics-Guided Transformer (RGT) that fuses \textit{global} image…
OpenPath: Open-Set Active Learning for Pathology Image Classification via Pre-trained Vision-Language Models
University of Electronic Science and Technology of China（电子科技大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Pathology, West China Second University Hospital, Sichuan University（四川大学病理科西昌第二大学医院部）；Department of Radiation Oncology, Sichuan Cancer Hospital and Institute, University of Electronic Science and Technology of China（四川癌症医院与研究所放射肿瘤科，电子科技大学）
DyMorph-B2I: Dynamic and Morphology-Guided Binary-to-Instance Segmentation for Renal Pathology
Southern University of Science and Technology（南方科技大学）；Vanderbilt University（范德比尔特大学）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；Weill Cornell Medicine（韦氏 Cornell 医学院）；Cornell Tech（康奈尔科技）；Northwell Health（北well 健康）
EviPathBench: Benchmarking Evidence Acquisition and Reasoning in Vision-Language Models for Whole-Slide Pathology
National University of Singapore（新加坡国立大学）；PuzzleLogic Pte Ltd（拼图逻辑私人有限公司）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳校区）；Peking Union Medical College Hospital（北京协和医院）
Prompt-Guided Foundation Model Tuning for Pathology Image Classification
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology（香港科技大学化学与生物工程系）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）；State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology（香港科技大学神经系统疾病国家重点实验室）
VIPER: An Expert-Curated Benchmark for Vision-Language Models in Veterinary Pathology
Harvard-MIT HST（哈佛-麻省理工卫生科学与技术部）；Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；COMPATH, University of Bern（伯尔尼大学COMPATH）；UC Davis（加州大学戴维斯分校）；University of Augsburg（奥格斯堡大学）；UT MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；TU Dresden（德累斯顿工业大学）；University of Lausanne（洛桑大学）
VISTA: Validation-Guided Integration of Spatial and Temporal Foundation Models with Anatomical Decoding for Rare-Pathology VCE Event Detection -- after competition results
National Cheng Kung University, Taiwan（国立成功大学, 台湾）；National Yang Ming Chiao Tung University, Taiwan（国立阳明交通大学, 台湾）
GRASPing Anatomy to Improve Pathology Segmentation
Karlsruhe Institute of Technology, Karlsruhe, Germany（卡尔斯鲁厄理工学院）；Helmholtz Information and Data Science School for Health, Germany（海德堡信息与数据科学健康学校）；Department of Nuclear Medicine, University of Duisburg-Essen（杜伊斯堡-埃森大学核医学系）；German Cancer Consortium (DKTK)- University Hospital Essen, Essen, Germany（德国癌症研究中心（DKTK）-埃森大学医院）
Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
Department of Pathology, College of Medicine, The Ohio State University Wexner Medical Center（病理学系，医学学院，俄亥俄州立大学韦克斯纳医学中心）；Department of Biomedical Engineering, The Ohio State University（生物医学工程系，俄亥俄州立大学）
Pathology-Guided Virtual Staining Metric for Evaluation and Training
Department of Systems Design Engineering（系统设计工程系）；Department of Applied Mathematics（应用数学系）；Cheriton School of Computer Science（切里顿计算机科学学院）；Department of Biology（生物学系）；School of Pharmacy（药学院）
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）
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Medicine Essen（埃森大学医学院）
When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted from these medical reports for training. This form of supervision limits the potential usage of models as they are unable to pick up on anomalies outside of their predefined set, thus, making it a necessity to retrain the classifier with additional data when faced with novel classes. In contrast, we investigate direct text supervision to break away from this closed set assumption. By…
PA-TCNet: Pathology-Aware Temporal Calibration with Physiology-Guided Target Refinement for Cross-Subject Motor Imagery EEG Decoding in Stroke Patients
School of Artificial Intelligence, Chongqing University of Technology（重庆理工大学人工智能学院）；Chongqing Key Laboratory of Embodied Intelligence Perception and Autonomous Learning for Humanoid Robots（重庆 embodied 智能感知与人形机器人自主学习关键实验室）；Key Laboratory of Advanced Equipment Intelligence of the Chongqing Education Commission（重庆市教育委员会先进设备智能关键实验室）；School of Smart Health, Chongqing Polytechnic University of Electronic Technology（重庆电子工程职业大学智能健康学院）；Department of Language Science and Technology, The Hong Kong Polytechnic University（香港理工大学语言科学与技术系）；School of Pharmacy and Bioengineering, Chongqing University of Technology（重庆理工大学药学院与生物工程学院）；School of Computer Science and Engineering, Chongqing University of Technology（重庆理工大学计算机科学与工程学院）
Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT)（先进磁共振诊断与治疗技术国家工程研究中心）；Med-X Research Institute, Shanghai Jiao Tong University（Med-X研究院，上海交通大学）；Department of Spine Surgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院脊柱外科部）；Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院放射科）；Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University（上海交通大学医学院附属仁济医院放射科）
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA（Athinoula A. Martinos 生物医学成像中心、麻省总医院和哈佛医学院，波士顿，美国）；Danish Research Centre for Magnetic Resonance, Department of Radiology and Nuclear Medicine（丹麦磁共振研究中心、放射学与核医学部门）；Copenhagen University Hospital– Amager and Hvidovre（哥本哈根大学医院–阿马格和赫维多尔）
QCAgent: An agentic framework for quality-controllable pathology report generation from whole slide image
School of Biomedical Engineering, Division of Life Sciences；Medicine, University of Science；Technology of China, Hefei, Anhui, 230026, P.R. China Center for Medical Imaging, Robotics, Analytic Computing \& Learning (MIRACLE), Suzhou Institute for Advance Research, USTC, 215123, P.R. China Chinese PLA General Hospital Ninth Medical Center, China Hong Kong University of Science
GALAR-TemporalNet v2: Anatomy-Guided Dual-Branch Temporal Classification with Bidirectional Mamba and Dual-Graph GCN for Video Capsule Endoscopy -- after competition results
School of Computer Science and Engineering, Kyungpook National University（韩国庆北国立大学计算机科学与工程学院）
