TCGA-PRAD前列腺癌病理数据集精炼版

Name: TCGA-PRAD前列腺癌病理数据集精炼版
Creator: maas
License: openrail

该数据集是一个针对TCGA-PRAD前列腺癌病理图像的精炼标注数据集，包含245个全切片图像（WSI）的切片级标签和区域级（ROI）空间标注，数据模态为病理图像与结构化标注文本；其主要用途是提供更准确的Gleason分级标签和透明化的病理推理，以支持AI病理模型的训练与验证，并促进前列腺癌的肿瘤分级研究和临床病理分析。

maas

魔搭社区

2025-12-05 更新

前列腺癌病理学

Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset

基本信息

大小

0.021667988 GB

许可

openrail

创建/更新时间

2025-12-05

资源简介

该数据集是对TCGA-PRAD前列腺癌数据集进行精炼和增强的病理标注数据集，由Codatta和DPath.ai平台联合全球病理学家协作构建。数据集包含245个经过重新标注的TCGA全切片图像（WSI），在原始TCGA标签基础上，提供了更准确的Gleason分级标签（如4+3、5+4）和区域级（ROI）空间标注，每个标注均附有病理学家的详细诊断推理。数据集包含CSV格式的切片级标签文件和GeoJSON格式的ROI标注文件，可用于训练高精度AI病理模型，支持前列腺癌的肿瘤分级、区域可视化及病理学研究。

原始链接

https://modelscope.cn/datasets/Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset

访问原始数据

官方服务

专家保障全链路合规授权与标注交付

陕川双基地 × 超十家三甲医院直连

从合规授权、采集治理、专业标注到数据集交付，我们提供全链路闭环服务，让高质量临床数据即拿即用。

⚡️ 需要原始数据或标注支持？联系我们获取定制方案。

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下载信息

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暂未开放

当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。

使用方式

数据集获取（ModelScope）

方式一：MsDataset（Python）

# 前置依赖: pip install modelscope
from modelscope.msdatasets import MsDataset

ds = MsDataset.load("Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset", subset_name="default", split="train")
print(ds)

方式二：CLI（命令行）

pip install modelscope
modelscope download dataset Codatta/Refined-TCGA-PRAD-Prostate-Cancer-Pathology-Dataset

数据集卡片摘录（源站）

Note: Our 245 TCGA cases are ones we identified as having potential for improvement.
We plan to upload them in two phases: the first batch of 138 cases, and the second batch of 107 cases in the quality review pipeline, we plan to upload them around early of January, 2025.

Dataset: A Second Opinion on TCGA PRAD Prostate Dataset Labels with ROI-Level Annotations

Overview

Exmaple of Annotated WSI
This dataset provides enhanced Gleason grading annotations for the TCGA PRAD prostate cancer dataset, supported by Region of Interest (ROI)-level spatial annotations. Developed in collaboration with Codatta and DPath.ai, where DPath.ai launched a dedicated community via Codatta to assemble a network of pathologists, this dataset improved accuracies and granularity of information in the original TCGA-PRAD slide-level labels. The collaborative effort enabled pathologists worldwide to contribute annotations, improving label reliability for AI model training and advancing pathology research. Unlike traditional labeling marketplaces, collaborators a.k.a pathologists retain ownership of the dataset, ensuring their contributions remain recognized, potentially rewarded and valuable within the community. Please cite the dataset in any publication or work to acknowledge the collaborative efforts of Codatta, DPath.ai, and the contributing pathologists.

Motivation

We discovered significant opportunities for data improvement in the diagnostic labels of the TCGA PRAD dataset, where:

Some labels could be enhanced with additional diagnostic labels a.k.a opinions.
Some labels lacked granular descriptions of the Gleason patterns.

This presents a challenge for AI pathology models, as reported high accuracy might reflect learning from improved labels. To address this:

Pathologists re-annotated slides to improve label quality.
ROI annotations were introduced to clearly differentiate Gleason tumor grades.
Each annotation is supported by detailed reasoning, providing transparency and justification for corrections.