Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction
Tongji University（同济大学）；Tongji Hospital（同济医院）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；Cancer Research UK Cambridge Institute（英国癌症研究剑桥研究所）；University of Cambridge（剑桥大学）
The characterization of Tumor MicroEnvironment (TME) is challenging due to its complexity and heterogeneity. Relatively consistent TME characteristics embedded within highly specific tissue features, render them difficult to predict. The capability to accurately classify TME subtypes is of critical significance for clinical tumor diagnosis and precision medicine. Based on the observation that tumors with different origins share similar microenvironment patterns, we propose PathoTME, a genomics-guided Siamese representation learning framework employing Whole Slide Image (WSI) for pan-cancer TME subtypes prediction. Specifically, we utilize Siamese network to leverage genomic information as a regularization factor to assist WSI embeddings learning during the training phase. Additionally, we employ Domain Adversarial Neural Network (DANN) to mitigate the impact of tissue type variations. To eliminate domain bias, a dynamic WSI prompt is designed to further unleash the model's capabilities. Our model achieves better performance than other state-of-the-art methods across 23 cancer types on TCGA dataset. Our code is available at https://github.com/Mengflz/PathoTME.
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis
The University of Tokyo（东京大学）；Institute of Science Tokyo（东京科学大学）；Tottori University（鸟取大学）；Biomy Inc.（Biomy公司）；Japanese Red Cross Medical Center（日本红十字会医疗中心）；Kanagawa Cancer Center（神奈川县癌症中心）；Kanagawa Cancer Center Research Institute（神奈川县癌症中心研究所）；National Cancer Center Exploratory Oncology Research & Clinical Trial Center（国立癌症中心探索性肿瘤学研究与临床试验中心）
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Xiangya Hospital, Central South University（中南大学湘雅医院）；University of Chinese Academy of Sciences（中国科学院大学）；Central South University（中南大学）
Molecular subtypes of breast cancer are important references to personalized clinical treatment. For cost and labor savings, only one of the patient's paraffin blocks is usually selected for subsequent immunohistochemistry (IHC) to obtain molecular subtypes. Inevitable sampling error is risky due to tumor heterogeneity and could result in a delay in treatment. Molecular subtype prediction from conventional H&E pathological whole slide images (WSI) using AI method is useful and critical to assist pathologists pre-screen proper paraffin block for IHC. It's a challenging task since only WSI level…
Classification of lung cancer subtypes on CT images with synthetic pathological priors
Research Center for Healthcare Data Science（医疗数据科学研究中心）；Zhejiang Lab（浙江大学实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Northwestern Polytechnical University（西北工业大学）；Institute of Computer Graphics and Vision（计算机图形学与视觉研究所）；Graz University of Technology（格拉茨工业大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Computer Science（计算机科学系）；Rutgers University（罗格斯大学）
CC-DCNet: Dynamic Convolutional Neural Network with Contrastive Constraints for Identifying Lung Cancer Subtypes on Multi-modality Images
Zhejiang Lab（之江实验室）；Institute of Computer Graphics and Vision（计算机图形与视觉研究所）；Graz University of Technology（格拉茨工业大学）；School of Computer Science and Engineering（计算机科学与工程学院）；Northwestern Polytechnical University（西北工业大学）；Zhejiang University School of Medicine（浙江大学医学院）；Sir Run Run Shaw Hospital（邵逸夫医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images
School 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 Science, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）理学院）
Kyushu University（九州大学）；Kyoto University Hospital（京都大学医院）；National Institute of Informatics（国立信息学研究所）
Automated digital histopathology image segmentation is an important task to help pathologists diagnose tumors and cancer subtypes. For pathological diagnosis of cancer subtypes, pathologists usually change the magnification of whole-slide images (WSI) viewers. A key assumption is that the importance of the magnifications depends on the characteristics of the input image, such as cancer subtypes. In this paper, we propose a novel semantic segmentation method, called Adaptive-Weighting-Multi-Field-of-View-CNN (AWMF-CNN), that can adaptively use image features from images with different magnifica…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences（中国医学科学院国家癌症中心/肿瘤医院）；University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
The spatial distributions of different types of cells could reveal a cancer cell growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key hallmarks of cancer. However, manually recognizing and localizing all the cells in pathology slides are almost impossible. In this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor, stromal and lymphocytes classification, and extraction of tumor microenvironment related features for …
King Abdullah II School for Information Technology, University of Jordan（约旦大学阿卜杜拉二世国王信息技术学院）
Meningioma brain tumour discrimination is challenging as many histological patterns are mixed between the different subtypes. In clinical practice, dominant patterns are investigated for signs of specific meningioma pathology; however the simple observation could result in inter- and intra-observer variation due to the complexity of the histopathological patterns. Also employing a computerised feature extraction approach applied at a single resolution scale might not suffice in accurately delineating the mixture of histopathological patterns. In this work we propose a novel multiresolution fea…
Nagoya Institute of Technology（名古屋工业大学）；Nagoya University Hospital（名古屋大学医院）；RIKEN（理化学研究所）
We propose a new method for cancer subtype classification from histopathological images, which can automatically detect tumor-specific features in a given whole slide image (WSI). The cancer subtype should be classified by referring to a WSI, i.e., a large-sized image (typically 40,000x40,000 pixels) of an entire pathological tissue slide, which consists of cancer and non-cancer portions. One difficulty arises from the high cost associated with annotating tumor regions in WSIs. Furthermore, both global and local image features must be extracted from the WSI by changing the magnifications of th…
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）
Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. Tumor region detection, subtype and grade classification are the fundamental diagnostic indicators for renal cell carcinoma (RCC) in whole-slide images (WSIs). However, pathological diagnosis is subjective, differences in observation and diagnosis between pathologists is common in hospitals with inadequate diagnostic capacity. The main challenge for developing deep learning based RCC diagnostic system is the la…
Neoadjuvant chemotherapy response prediction using pretreatment diffusion and contrast-enhanced magnetic resonance imaging with clinical variables
University of Oviedo（奥维耶多大学）；University of A Coruña（拉科鲁尼亚大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Purdue University（普渡大学）；Asturias Central University Hospital (HUCA)（阿斯图里亚斯中央大学医院）；Cabueñes University Hospital (CAHU)（卡布埃涅斯大学医院）
ETH Zurich（苏黎世联邦理工学院）；EPFL（洛桑联邦理工学院）；IBM Research（IBM研究院）；IRCCS-Fondazione Pascale（IRCCS-帕斯卡尔基金会）；ICAR-CNR（意大利国家研究理事会ICAR研究所）
Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization in tissue via cell-graphs to tumor grades. Though cell organization is imperative, it is insufficient to entirely represent the histopathological structure. We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue…
A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools
Department of Pathology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China（北京清华长庚医院病理科，临床医学学院，清华大学医学院，清华大学，北京，中国）；Shenzhen International Graduate School, Tsinghua University, Shenzhen, China（深圳国际研究生院，清华大学，深圳，中国）
A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification
University of Leeds（利兹大学）；Leeds Institute of Medical Research at St James's（圣詹姆斯利兹医学研究所）；Leeds Cancer Centre（利兹癌症中心）；St James's University Hospital（圣詹姆斯大学医院）；Leeds Teaching Hospitals NHS Trust（利兹教学医院国民保健信托基金）；National Pathology Imaging Cooperative (NPIC)（国家病理影像合作组织）
A Hierarchical Geometry-guided Transformer for Histological Subtyping of Primary Liver Cancer
Department of Pathology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, China（病理科、南京鼓楼医院、南京大学附属医院）；Department of Biostatistics, School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029（生物统计学系、公共卫生学院）
Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy
Institut Curie（居里研究所）；PSL Research University（巴黎文理研究大学）；Paris-Saclay University（巴黎-萨克雷大学）；Inserm（法国国家健康与医学研究院）；Gustave Roussy（古斯塔夫·鲁西研究所）；Centre Léon Bérard（莱昂·贝拉尔中心）；HCL（里昂民用医院）；University of Paris（巴黎大学）
LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
Institute of Pathology, Charité – Universitätsmedizin Berlin（柏林夏里特医学院病理学研究所）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院柏林卫生研究所）；Aignostics GmbH（Aignostics有限公司）；Machine Learning Group, Technical University of Berlin（柏林工业大学机器学习组）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）；MVZ HPH Institut für Pathologie und Hämatopathologie GmbH（HPH病理及血液病理诊断中心有限公司）
Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)
Washington University in St. Louis（圣路易斯华盛顿大学）；Washington University School of Medicine（华盛顿大学医学院）；Mallinckrodt Institute of Radiology（马林克罗特放射学研究所）；The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）
Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy
Aignostics, Germany（Aignostics，德国）；Institute of Pathology, Charité – Universitätsmedizin Berlin, Germany（柏林夏里特医学院病理学研究所）；Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Germany（柏林夏里特医学院柏林健康研究所）；Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, MA, US（哈佛医学院麻省总医院病理学系）；Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, US（梅奥诊所检验医学与病理学系）；Machine Learning Group, Technische Universität Berlin, Germany（柏林工业大学机器学习组）；BIFOLD – Berlin Institute for the Foundations of Learning and Data, Germany（柏林学习与数据基础研究所）；Department of Artificial Intelligence, Korea University, Republic of Korea（高丽大学人工智能系）；Max-Planck Institute for Informatics, Germany（马克斯·普朗克信息学研究所）；German Cancer Research Center (DKFZ) & German Cancer Consortium (DKTK), Berlin & Munich Partner Sites, Germany（德国癌症研究中心及德国癌症联盟柏林和慕尼黑合作站点）；Institute of Pathology, Ludwig-Maximilians-Universität München, Germany（慕尼黑大学病理学研究所）；Bavarian Cancer Research Center (BZKF), Germany（巴伐利亚癌症研究中心）
