通知：联邦节假日调休：哥伦布日 / 原住民日（Columbus Day / Indigenous Peoples' Day），今日（13日）无更新。通知：联邦节假日调休：哥伦布日 / 原住民日（Columbus Day / Indigenous Peoples' Day），今日（13日）无更新。
arXiv 2405.07905eess.IVcs.CV
PLUTO：病理学通用Transformer
PLUTO: Pathology-Universal Transformer
PathAI（派斯艾公司）
Dinkar Juyal, Harshith Padigela, Chintan Shah, Daniel Shenker, Natalia Harguindeguy, Yi Liu, Blake Martin, Yibo Zhang, Michael Nercessian, Miles Markey, Isaac F… 展开作者
Dinkar Juyal, Harshith Padigela, Chintan Shah, Daniel Shenker, Natalia Harguindeguy, Yi Liu, Blake Martin, Yibo Zhang, Michael Nercessian, Miles Markey, Isaac Finberg, Kelsey Luu, Daniel Borders, Syed Ashar Javed, Emma Krause, Raymond Biju, Aashish Sood, Allen Ma, Jackson Nyman, John Shamshoian, Guillaume Chhor, Darpan Sanghavi, Marc Thibault, Limin Yu, Fedaa Najdawi, Jennifer A. Hipp, Darren Fahy, Benjamin Glass, Eric Walk, John Abel, Harsha Pokkalla, Andrew H. Beck, Sean Grullon
Pathology is the study of microscopic inspection of tissue, and a pathology diagnosis is often the medical gold standard to diagnose disease. Pathology images provide a unique challenge for computer-vision-based analysis: a single pathology Whole Slide Image (WSI) is gigapixel-sized and often contains hundreds of thousands to millions of objects of interest across multiple resolutions. In this work, we propose PathoLogy Universal TransfOrmer (PLUTO): a light-weight pathology FM that is pre-trained on a diverse dataset of 195 million image tiles collected from multiple sites and extracts meaningful representations across multiple WSI scales that enable a large variety of downstream pathology tasks. In particular, we design task-specific adaptation heads that utilize PLUTO's output embeddings for tasks which span pathology scales ranging from subcellular to slide-scale, including instance segmentation, tile classification, and slide-level prediction. We compare PLUTO's performance to other state-of-the-art methods on a diverse set of external and internal benchmarks covering multiple biologically relevant tasks, tissue types, resolutions, stains, and scanners. We find that PLUTO matches or outperforms existing task-specific baselines and pathology-specific foundation models, some of which use orders-of-magnitude larger datasets and model sizes when compared to PLUTO. Our findings present a path towards a universal embedding to power pathology image analysis, and motivate further exploration around pathology foundation models in terms of data diversity, architectural improvements, sample efficiency, and practical deployability in real-world applications.
Adaptive Patching for High-resolution Image Segmentation with Transformers
Hokkaido University（北海道大学）；Oak Ridge National Laboratory（橡树岭国家实验室）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；Vanderbilt University（范德堡大学）；Information Initiative Center Hokkaido University（北海道大学信息倡议中心）
DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images
School of Electrical Engineering, Korea University（高丽大学电气工程学院）；Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine（成均馆大学医学院江北三星医院）；Pathology Center, Seegene Medical Foundation（Seegene医学基金会病理中心）
Automatic Cardiac Pathology Recognition in Echocardiography Images Using Higher Order Dynamic Mode Decomposition and a Vision Transformer for Small Datasets
Universidad Politécnica de Madrid（马德里理工大学）；Centro Nacional de Investigaciones Cardiovasculares (CNIC)（国家心血管研究中心）；Universidad Complutense de Madrid（马德里康普顿斯大学）；Center for Computational Simulation (CCS)（计算仿真中心）
LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images
Masaryk University, Faculty of Informatics（马萨里克大学信息学院）；Masaryk University, Institute for Computer Science（马萨里克大学计算机科学研究所）；Masaryk Memorial Cancer Institute（马萨里克纪念癌症研究所）
DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture
School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）；School of Management, University of Science and Technology of China（中国科学技术大学管理学院）；School of Population Health, UNSW Sydney（新南威尔士大学悉尼分校人口健康学院）；Genmab Inc.（杰特贝公司）
A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology
Department of Biomedical Engineering, Eindhoven University of Technology（生物医学工程系，埃因霍温理工大学）；Mathematics and Computer Science Department, Eindhoven University of Technology（数学与计算机科学系，埃因霍温理工大学）
CircleFormer: Circular Nuclei Detection in Whole Slide Images with Circle Queries and Attention
Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences（中国科学院合肥物质科学研究院智能机械研究所）；Institutes of Physical Science and Information Technology, Anhui University（安徽大学物质科学与信息技术研究院）；School of Computer and Artificial Intelligence, Zhengzhou University（郑州大学计算机与人工智能学院）
Pathology Image Restoration via Mixture of Prompts
ShanghaiTech University（上海科技大学）；Shanghai Jiao Tong University（上海交通大学）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；Nanjing University of Information Science and Technology（南京信息工程大学）
Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics
University of Concordia（康考迪亚大学）；University of Montreal（蒙特利尔大学）；Sunnybrook Health Science Centre（桑尼布鲁克健康科学中心）；Kingston General Hospital（金斯顿总医院）；Mila–Quebec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）
Virchow: A Million-Slide Digital Pathology Foundation Model
Paige；Microsoft Research（微软研究院）；NSW Health Pathology（新南威尔士州卫生病理学）；St George Hospital（圣乔治医院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）；University of Rochester（罗切斯特大学）
