PathOrchestra
包含30万张病理切片图像，覆盖20种组织器官，用于训练自监督病理基础模型，支持多种癌症相关临床任务。
基本信息
资源简介
PathOrchestra数据集由上海人工智能实验室等多个机构共同创建，包含30万张来自20种不同组织和器官的病理切片图像，数据源于三个中心的内部收藏和公共数据集。该数据集用于训练PathOrchestra模型，通过自监督学习在无标签数据上学习高质量特征表示，涵盖数字切片预处理、全癌分类、病变识别、多癌亚型分类、生物标志物评估、基因表达预测和结构化报告生成等临床任务，旨在促进计算病理学发展。
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使用方式
数据集说明
PathOrchestra 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2503.24345v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:The complexity and variability inherent in high-resolution pathological images present significant challenges in computational pathology. While pathology foundation models leveraging AI have catalyzed transformative advancements, their development demands large-scale datasets, considerable storage capacity, and substantial computational resources. Furthermore, ensuring their clinical applicability and generalizability requires rigorous validation across a broad spectrum of clinical tasks. Here, we present PathOrchestra, a versatile pathology foundation model trained via self-supervised learning on a dataset comprising 300K pathological slides from 20 tissue and organ types across multiple centers. The model was rigorously evaluated on 112 clinical tasks using a combination of 61 private and 51 public datasets. These tasks encompass digital slide preprocessing, pan-cancer classification, lesion identification, multi-cancer subtype classification, biomarker assessment, gene expression prediction, and the generation of structured reports. PathOrchestra demonstrated exceptional performance across 27,755 WSIs and 9,415,729 ROIs, achieving over 0.950 accuracy in 47 tasks, including pan-cancer classification across various organs, lymphoma subtype diagnosis, and bladder cancer screening. Notably, it is the first model to generate structured reports for high-incidence colorectal cancer and diagnostically complex lymphoma-areas that are infrequently addressed by foundational models but hold immense clinical potential. Overall, PathOrchestra exemplifies the feasibility and e
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