Whole slide imaging is fundamental to biomedical microscopy and computational pathology. Previously, learning representations for gigapixel-sized whole slide images (WSIs) has relied on multiple instance learning with weak labels, which do not annotate the diverse morphologic features and spatial heterogeneity of WSIs. A high-quality self-supervised learning method for WSIs would provide transferable visual representations for downstream computational pathology tasks, without the need for dense annotations. We present Slide Pre-trained Transformers (SPT) for gigapixel-scale self-supervision of WSIs. Treating WSI patches as tokens, SPT combines data transformation strategies from language and vision modeling into a general and unified framework to generate views of WSIs for self-supervised pretraining. SPT leverages the inherent regional heterogeneity, histologic feature variability, and information redundancy within WSIs to learn high-quality whole slide representations. We benchmark SPT visual representations on five diagnostic tasks across three biomedical microscopy datasets. SPT significantly outperforms baselines for histopathologic diagnosis, cancer subtyping, and genetic mutation prediction. Finally, we demonstrate that SPT consistently improves whole slide representations when using off-the-shelf, in-domain, and foundational patch encoders for whole slide multiple instance learning.
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
The Hong Kong University of Science and Technology（香港科技大学）；Southern Medical University（南方医科大学）；Guangdong Provincial Key Laboratory of Molecular Tumor Pathology（广东省分子肿瘤病理学重点实验室）；Jinfeng Laboratory（金凤实验室）；Shandong First Medical University（山东第一医科大学）；Shandong Provincial Qianfoshan Hospital（山东省千佛山医院）；The Third Affiliated Hospital of Kunming Medical University（昆明医科大学第三附属医院）；Yunnan Cancer Hospital（云南省肿瘤医院）；The Chinese University of Hong Kong（香港中文大学）；Prince of Wales Hospital（威尔士亲王医院）；Sun Yat-sen University（中山大学）
Weisberg Division of Computer Science, Marshall University（马歇尔大学魏斯伯格计算机科学分部）；Kitware, Inc.（Kitware公司）；Emory University School of Medicine（埃默里大学医学院）；National Cancer Institute（美国国家癌症研究所）；Cairo University（开罗大学）；Northwestern University Feinberg School of Medicine（西北大学费恩伯格医学院）
Dynamic Residual Encoding with Slide-Level Contrastive Learning for End-to-End Whole Slide Image Representation
School of Computer Science and Enginecring, Central South University（计算机科学与工程学院，中南大学）；School of Computer Science（计算机科学学院）；Enginecring, Central South University（工程学院，中南大学）
Enhancing Whole Slide Image Classification through Supervised Contrastive Domain Adaptation
HUMAN-tech；Universidad Politécnica de Valencia (UPV)（瓦伦西亚理工大学）；Valencian Graduate School and Research Network for Artificial Intelligence (valgrAI)（瓦伦西亚人工智能研究生学院及研究网络）
SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；School of Software, Hefei University of Technology（合肥工业大学软件学院）；Image Processing Center, School of Astronautics, Beihang University（北京航空航天大学宇航学院图像处理中心）；Beijing Advanced Innovation Center on Biomedical Engineering（北京生物医学工程高精尖创新中心）
Benchmarking Pathology Feature Extractors for Whole Slide Image Classification
University of St Andrews（圣安德鲁斯大学）；Else Kröner Fresenius Center for Digital Health（埃尔瑟·克勒纳·弗雷森纽斯数字健康中心）；TUD Dresden University of Technology（德累斯顿工业大学）；University Hospital Aachen（亚琛大学医院）；University Hospital Heidelberg（海德堡大学医院）；University Hospital Dresden（德累斯顿大学医院）
Nonparametric Distribution Matching for Self-Supervised Whole-Slide Image Condensation
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Washington State University（华盛顿州立大学）；Hanoi Medical University（河内医科大学）；VinUniversity（文森大学）；Hanoi University of Science and Technology（河内科技大学）
