A crucial step to efficiently integrate Whole Slide Images (WSIs) in computational pathology is assigning a single high-quality feature vector, i.e., one embedding, to each WSI. With the existence of many pre-trained deep neural networks and the emergence of foundation models, extracting embeddings for sub-images (i.e., tiles or patches) is straightforward. However, for WSIs, given their high resolution and gigapixel nature, inputting them into existing GPUs as a single image is not feasible. As a result, WSIs are usually split into many patches. Feeding each patch to a pre-trained model, each WSI can then be represented by a set of patches, hence, a set of embeddings. Hence, in such a setup, WSI representation learning reduces to set representation learning where for each WSI we have access to a set of patch embeddings. To obtain a single embedding from a set of patch embeddings for each WSI, multiple set-based learning schemes have been proposed in the literature. In this paper, we evaluate the WSI search performance of multiple recently developed aggregation techniques (mainly set representation learning techniques) including simple average or max pooling operations, Deep Sets, Memory networks, Focal attention, Gaussian Mixture Model (GMM) Fisher Vector, and deep sparse and binary Fisher Vector on four different primary sites including bladder, breast, kidney, and Colon from TCGA. Further, we benchmark the search performance of these methods against the median of minimum distances of patch embeddings, a non-aggregating approach used for WSI retrieval.
WsiCaption: Multiple Instance Generation of Pathology Reports for Gigapixel Whole-Slide Images
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Research Center for Industries of the Future, Westlake University（西湖大学未来产业研究中心）；School of Engineering, Westlake University（西湖大学工程学院）
St. Olavs hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；NTNU - Norwegian University of Science and Technology（挪威科技大学）；University of Helsinki（赫尔辛基大学）；SINTEF Digital
WSI-Agents: A Collaborative Multi-Agent System for Multi-Modal Whole Slide Image Analysis
College of Artificial Intelligence, Shenzhen University, China（深圳大学人工智能学院）；School of Computer Science, University of Nottingham Ningbo China, China（诺丁汉宁波大学计算机学院）；College of Computer Science and Software Engineering, Shenzhen University, China（深圳大学计算机科学与软件工程学院）；Department of Electrical Engineering, City University of Hong Kong, Hong Kong（香港城市大学电气工程系）；Wuyi University, China（武夷大学）
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
Karolinska Institutet（卡罗林斯卡学院）；FFEI Ltd（FFEI有限公司）；University of Turku（图尔库大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；Aarhus University Hospital（奥胡斯大学医院）；Aarhus University（奥胡斯大学）；Tampere University（坦佩雷大学）
CRISP -- Clustering-Based Redundancy-Reduced Instance Sampling for Pathology Case Representation and Retrieval
Kimia Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA（Kimia实验室，人工智能与信息学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；DICE Lab, Department of Electrical and Computer Engineering, University of Illinois Chicago, IL, USA（DICE实验室，电气与计算机工程系，伊利诺伊大学芝加哥分校，伊利诺伊州，美国）；MD Kimia Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA（MD Kimia实验室，人工智能与信息学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；PhD Kimia Lab, Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, USA（PhD Kimia实验室，人工智能与信息学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；Division of Computational Pathology and Informatics, Mayo Clinic, Rochester, MN, USA（计算病理学与信息学部，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA（实验室医学与病理学系，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Breast and Melanoma Surgical Oncology, Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA（乳腺和黑色素瘤外科肿瘤学系，综合癌症中心，梅奥诊所，罗切斯特，明尼苏达州，美国）；Department of Oncology, Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA（肿瘤学系，综合癌症中心，梅奥诊所，罗切斯特，明尼苏达州，美国）；PhD H.R. Tizhoosh
Paired Uterine Whole-Slide Images and Pathology Reports for Multimodal Computational Pathology
Institute of Pathology, Technical University of Munich（慕尼黑工业大学病理研究所）；Computer Aided Medical Procedures (CAMP), Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序（CAMP））；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Department of Human Pathology, Juntendo University Graduate School of Medicine（顺天堂大学医学研究生院人体病理学部）；The Hong Kong University of Science and Technology（香港科技大学）；Munich Data Science Institute (MDSI)（慕尼黑数据科学研究所）
Unlocking the Potential of Digital Pathology: Novel Baselines for Compression
German Cancer Research Center (DKFZ)（德国癌症研究中心 (DKFZ)）；Heidelberg University（海德堡大学）；Ulm University Medical Centre（乌尔姆大学医学中心）；Technical University of Munich（慕尼黑工业大学）；University Medicine Essen（埃森大学医学中心）；Heidelberg University Hospital（海德堡大学医院）；PixelMed Publishing
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）
From slides to AI-ready maps: Standardized multi-layer tissue maps as metadata for artificial intelligence in digital pathology
Medical University of Graz（格拉茨医科大学）；Graz University of Technology（格拉茨工业大学）；University of Maribor（马里博尔大学）；Masaryk University（马萨里克大学）；Masaryk Memorial Cancer Institute（马萨里克纪念癌症研究所）；University of Natural Resources and Life Sciences Vienna（维也纳自然资源与生命科学大学）；BBMRI-ERIC（欧洲生物样本库与生物分子资源研究基础设施联盟）；University Hospital RWTH Aachen（亚琛工业大学医院）；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）；EMPAIA International e.V.（EMPAIA国际协会）；European Society of Digital and Integrative Pathology（欧洲数字与整合病理学会）
Brigham and Women’s Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院博德研究所）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）
