We present a novel diffusion-based approach to generate synthetic histopathological Whole Slide Images (WSIs) at an unprecedented gigapixel scale. Synthetic WSIs have many potential applications: They can augment training datasets to enhance the performance of many computational pathology applications. They allow the creation of synthesized copies of datasets that can be shared without violating privacy regulations. Or they can facilitate learning representations of WSIs without requiring data annotations. Despite this variety of applications, no existing deep-learning-based method generates WSIs at their typically high resolutions. Mainly due to the high computational complexity. Therefore, we propose a novel coarse-to-fine sampling scheme to tackle image generation of high-resolution WSIs. In this scheme, we increase the resolution of an initial low-resolution image to a high-resolution WSI. Particularly, a diffusion model sequentially adds fine details to images and increases their resolution. In our experiments, we train our method with WSIs from the TCGA-BRCA dataset. Additionally to quantitative evaluations, we also performed a user study with pathologists. The study results suggest that our generated WSIs resemble the structure of real WSIs.
Norwegian University of Science and Technology（挪威科技大学）；St. Olavs Hospital, Trondheim University Hospital（圣奥拉夫医院，特隆赫姆大学医院）；SINTEF Digital（挪威科技工业研究院数字研究所）
Brigham and Women’s Hospital（布里格姆妇女医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院布罗德研究所）；Department of Pathology（病理系）；Northwell Health（诺斯韦尔健康）；Department of Biomedical Informatics（生物医学信息学系）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Cancer Program（癌症项目）；Cancer Data Science（癌症数据科学）
University College London（伦敦大学学院）；Brigham and Women's Hospital（布莱根妇女医院）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛-麻省理工博德研究所）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；University of Warwick（华威大学）；University Hospitals Coventry and Warwickshire NHS Trust（考文垂与沃里克郡国民保健服务信托大学医院）；The Alan Turing Institute（艾伦·图灵研究所）
Xi'an Jiao tong University（西安交通大学）；School of Electronic and Information Engineering, Xi'an Jiao tong University（西安交通大学电子与信息工程学院）；the First Affiliated Hospital of Xi’an Jiaotong University（西安交通大学第一附属医院）；Xidian University（西安电子科技大学）
Sorbonne Université（索邦大学）；Paris Brain Institute - ICM（巴黎脑研究所-ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共援助医院）；Hôpital de la Pitié Salpêtrière（皮蒂耶-萨尔佩特里埃医院）
Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images
Center for Applied Artificial Intelligence, University of Kentucky（应用人工智能中心，肯塔基大学）；Sanders-Brown Center on Aging, University of Kentucky（老龄化桑德斯-布朗中心，肯塔基大学）；Department of Electrical and Computer Engineering, University of Kentucky（电气与计算机工程系，肯塔基大学）；Division of Neuropathology, University of Pittsburgh（神经病理学部，匹兹堡大学）；Department of Biomedical Informatics, Emory University（生物医学信息学系，埃默里大学）；Department of Pathology and Laboratory Medicine, University of California Davis（病理学与实验室医学系，加州大学戴维斯分校）；Department of Laboratory Medicine and Pathology, University of Texas Health（实验室医学与病理学系，德克萨斯大学健康科学中心）
ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所-巴黎脑研究所-ICM）；Inserm（法国国家医学院）；CNRS（法国国家科学研究中心）；APHP（法国国家卫生与医学研究院）；Hôpital de la Pitié Salpêtrière（皮蒂埃萨尔佩特里医院）；DMU Neuroscience（DMU神经科学）；Inria（法国国家信息与自动化技术研究院）
Generalisation of automatic tumour segmentation in histopathological whole-slide images across multiple cancer types
Institute for Cancer Genetics and Informatics（癌症基因组学与信息学研究所）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；Department of Gastrointestinal（胃肠科部）；Institute of Clinical Medicine（临床医学研究所）；Department of Gynaecological Oncology（妇科肿瘤科部）；Amsterdam University Medical Centres（阿姆斯特丹大学医学中心）；Department of Obstetrics and Gynaecology（妇产科部）；Comprehensive Cancer Center Innsbruck（因斯布鲁克综合癌症中心）；Innsbruck Medical University（因斯布鲁克医学院）；Vestre Viken Hospital Trust（维斯特维肯医院信托）；Department of Medical Biology（医学生物学部）；UiT The Arctic University of Norway（挪威北极大学（UiT））；Department of Clinical Pathology（临床病理学部）
Leveraging Pre-trained Models for FF-to-FFPE Histopathological Image Translation
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；Jinfeng Laboratory（金凤实验室）
