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arXiv 2605.25175cs.CV
差异最小化提升数字病理学中的跨医院鲁棒性
Discrepancy Minimization Improves Cross-Hospital Robustness in Digital Pathology
Reichman University, Herzliya, Israel（以色列海法大学）；Institute of Pathology, Sheba Medical Center, Ramat-Gan, Israel（以色列沙巴医疗中心病理研究所）；Technion - Israel Institute of Technology, Haifa, Israel（以色列技术学院）
Ben Vardi, Dana Schonberger, Yuval Friedmann, Zohar Yakhini, Iris Barshack, Alexander Loebel, Ariel Shamir
Pathology foundation models (PFMs) have advanced rapidly in recent years and support training classifiers for a range of histopathology tasks. However, their robustness across hospitals remains limited: performance often degrades when training a classifier on data from one hospital and evaluating it on another target hospital. We address this challenge by fine-tuning PFMs with a local maximum mean discrepancy (LMMD) objective that applies to two settings: domain adaptation, where unlabeled target-hospital data is available, and domain generalization, where target-hospital data is unavailable at all. Experiments at both the patch- and slide-level show consistent improvements across multiple PFMs and tasks.
A Utility-preserving De-identification Pipeline for Cross-hospital Radiology Data Sharing
Shandong University（山东大学）；Australian National University（澳大利亚国立大学）；Hong Kong University of Science and Technology（香港科技大学）；Curtin University（科廷大学）；Edith Cowan University（埃迪斯科文大学）；University of Western Australia（西澳大利亚大学）
University of Waterloo（滑铁卢大学）；Ontario Tech University（安大略理工大学）；Mayo Clinic（梅奥诊所）
Whole Slide Images (WSIs) in digital pathology are used to diagnose cancer subtypes. The difference in procedures to acquire WSIs at various trial sites gives rise to variability in the histopathology images, thus making consistent diagnosis challenging. These differences may stem from variability in image acquisition through multi-vendor scanners, variable acquisition parameters, and differences in staining procedure; as well, patient demographics may bias the glass slide batches before image acquisition. These variabilities are assumed to cause a domain shift in the images of different hospi…
From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology
Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；Helmholtz Munich – German Research Center for Environment and Health（亥姆霍兹慕尼黑中心-德国环境与健康研究中心）；Technical University of Munich（慕尼黑工业大学）；University Hospital Aachen（亚琛大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University of Leeds（利兹大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）；Technische Universität Dresden（德累斯顿工业大学）
The use of ImageNet pre-trained networks is becoming widespread in the medical imaging community. It enables training on small datasets, commonly available in medical imaging tasks. The recent emergence of a large Chest X-ray dataset opened the possibility for learning features that are specific to the X-ray analysis task. In this work, we demonstrate that the features learned allow for better classification results for the problem of Tuberculosis detection and enable generalization to an unseen dataset. To accomplish the task of feature learning, we train a DenseNet-121 CNN on 112K images fro…
Generative Deep Learning for Computational Destaining and Restaining of Unregistered Digital Pathology Images
Department of Computer Science University of California Irvine, CA, USA（计算机科学系 加州大学 伊藤市 加州 USA）；Department of Pathology Stanford University Stanford, CA, USA（病理学系 斯坦福大学 斯坦福市 加州 USA）
Xi’an Jiaotong University（西安交通大学）；Pennsylvania State University（宾夕法尼亚州立大学）；the First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）
Digital pathology plays a crucial role in the development of artificial intelligence in the medical field. The digital pathology platform can make the pathological resources digital and networked, and realize the permanent storage of visual data and the synchronous browsing processing without the limitation of time and space. It has been widely used in various fields of pathology. However, there is still a lack of an open and universal digital pathology platform to assist doctors in the management and analysis of digital pathological sections, as well as the management and structured descripti…
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València（瓦伦西亚理工大学以人为中心技术大学研究院）；valgrAI: Valencian Graduate School and Research Network of Artificial Intelligence（瓦伦西亚人工智能研究生院及研究网络）；ÉTS Montréal（蒙特利尔工程学院）
Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fréchet Domain Distance
Center for Medical Imaging Science and Visualization, Linköping University, University Hospital（林雪平大学 医学影像科学与可视化中心，大学医院）；Department of Science and Technology, Linköping University（林雪平大学 科学技术系）；Department of Biomedical and Clinical Sciences, Linköping University（林雪平大学 生物医学与临床科学系）；Sectra AB（塞克特拉公司）
Feature-level Site Leakage Reduction for Cross-Hospital Chest X-ray Transfer via Self-Supervised Learning
University of Mentouri Brothers Constantine 1（君士坦丁兄弟门图里大学）；LRIA Laboratory, University of Science and Technology Houari Boumediene (USTHB)（LRIA实验室，胡阿里·布迈丁科技大学）；LSIACIO Laboratory, University of Mentouri Brothers Constantine 1（LSIACIO实验室，君士坦丁兄弟门图里大学）；University of Paris-VIII-Vincennes-Saint-Denis（巴黎第八大学）
Label-Free Foundational Model Selection for Medical Image Classification under Distribution Shift via Pseudo Label Discrepancy
Universidad de Buenos Aires（布宜诺斯艾利斯大学）；CONICET（阿根廷国家科学技术研究委员会）；Universidad Nacional del Litoral（国立 littoral 大学）；Research Institute for Signals, Systems and Computational Intelligence（信号、系统与计算智能研究所）
Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology s…
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
University of Stavanger（斯塔万格大学）；University Medical Center Rotterdam（鹿特丹大学医学中心）；Erasmus MC Cancer Institute（伊拉斯姆斯大学医学中心癌症研究所）；Stavanger University Hospital（斯塔万格大学医院）；INCLIVA Biomedical Research Institute（INCLIVA生物医学研究所）；University of Valencia（瓦伦西亚大学）
CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration
Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；RWTH Aachen University（亚琛工业大学）；Medizinische Hochschule Hannover（汉诺威医学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Charité – Universitätsmedizin Berlin（柏林夏里特医学院）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）
ZeroSlide: Is Zero-Shot Classification Adequate for Lifelong Learning in Whole-Slide Image Analysis in the Era of Pathology Vision-Language Foundation Models?
Nara Institute of Science and Technology（奈良科学技术研究所）
BRIGHT: A Collaborative Generalist-Specialist Foundation Model for Breast Pathology
Department of Breast Pathology and Laboratory, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin’s Clinical Research Center for Cancer, West Huanhu Road, Tianjin, China（天津医科大学肿瘤医院乳腺病理科及实验室，国家癌症临床研究中心，天津医科大学乳腺癌预防与治疗重点实验室，天津医科大学，教育部，天津癌症临床研究中心，西湖南路，天津，中国）；Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China（广东省人工智能在医学影像分析与应用重点实验室，广东省人民医院（广东省医学科学院），南方医科大学，广州，中国）
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）
How similar are two images? In computational pathology, where Whole Slide Images (WSIs) of digitally scanned tissue samples from patients can be multi-gigapixels in size, determination of degree of similarity between two WSIs is a challenging task with a number of practical applications. In this work, we explore a novel strategy based on kernelized Maximum Mean Discrepancy (MMD) analysis for determination of pairwise similarity between WSIs. The proposed approach works by calculating MMD between two WSIs using kernels over deep features of image patches. This allows representation of an entire…
Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been proposed to help radiologists automatically quantify COVID-19 pathologies on longitudinal CT scans. However, the learning process of deep learning methods demands extensive training data to learn the complex characteristics of infected regions over longitudinal scans. It is challenging to collect a large-scale dataset, especially for longitudinal training. In this study, we want to address this problem by proposing …
Pathology-Informed Latent Diffusion Model for Anomaly Detection in Lymph Node Metastasis
School of Electrical Engineering, Korea University, Seoul 02841, Korea（韩国大学电子工程学院）；Department of Pathology, Korea University Anam Hospital and Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02841, Korea（韩国大学医学院病理学系）；Department of Hospital Pathology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea（韩国天主大学医学院圣玛丽医院医院病理学系）
PathBench: A comprehensive comparison benchmark for pathology foundation models towards precision oncology
Hong Kong University of Science and Technology（香港科技大学）；Fourth Military Medical University（第四军医大学）；Chinese University of Hong Kong（香港中文大学）；Harvard University（哈佛大学）
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Department of Biomedical Engineering, Linköping University, Sweden（生物医学工程系，利厄普大学）；Center for Medical Image Science and Visualization, Linköping University, Sweden（医学影像科学与可视化中心，利厄普大学）；Crown Princess Victoria Children’s Hospital and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（维多利亚公主儿童医院和健康、医学与关怀科学系，利厄普大学）；Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（放射科和健康、医学与关怀科学系，利厄普大学）；Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden（肿瘤病理学系，卡罗林斯卡研究所，索纳）；Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Sweden（临床病理学和癌症诊断系，卡罗林斯卡大学医院）；Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Sweden（放射物理系和医学与健康科学系，利厄普大学）
FedDA-TSformer: Federated Domain Adaptation with Vision TimeSformer for Left Ventricle Segmentation on Gated Myocardial Perfusion SPECT Image
Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）；Xiangya Hospital, Central South University（中南大学湘雅医院）；Chang Bing Show Chwan Memorial Hospital（彰滨秀传纪念医院）；The First Affiliated Hospital of Nanjing Medical University（南京医科大学第一附属医院）；Health Research Institute, Michigan Technological University（密歇根理工大学健康研究所）
