FreeTumor
FreeTumor是一个用于合成肝脏、胰腺、肾脏肿瘤及COVID-19病变的医学影像数据集，支持肿瘤位置模拟，适用于肿瘤检测与分割研究。
基本信息
资源简介
FreeTumor是一个用于大规模肿瘤合成的简单基线数据集，支持肝脏肿瘤、胰腺肿瘤、肾脏肿瘤和COVID-19等多种病变的合成。数据以医学影像模态呈现，用于肿瘤位置模拟和合成任务，旨在为肿瘤检测、分割等研究提供训练数据。
下载信息
注册下载
Tips: 该数据集需要在对应的数据源网站注册通过后，才能进行数据下载，注册有对应要求，或者需要收费。
暂未开放公开下载
Tips: 该数据集属于公开下载，应该可以免费公开下载。
免登录有偿下载
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提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放千方医数集，医疗数据集部分，是为社区服务的公开医疗数据集搜索引擎，并不存储或者下载原始的任何数据。 如果您有其他医疗数据需求，可以和客服联系，或者下工单。我们有强大的三甲医疗机构帮助您提供个性化的医疗数据定制、采集、标注服务。
使用方式
数据集获取
git clone https://github.com/Luffy03/FreeTumor.git
curl -L -o repo.zip https://github.com/Luffy03/FreeTumor/archive/refs/heads/main.zip
unzip repo.zip
源站 README 摘录（使用方式）
<div align=“center”>
<h1>[Nature Communications] Large-Scale Generative Tumor Synthesis in Computed Tomography Images for Improving Tumor Recognition</h1>
Paper link: https://doi.org/10.1038/s41467-025-66071-6.
Tumor is a leading cause of death worldwide, with an estimated 10 million deaths attributed to tumor-related diseases every year. AI-driven tumor recognition unlocks new possibilities for more precise and intelligent tumor screening and diagnosis. However, the progress is heavily hampered by the scarcity of annotated datasets, which demands extensive annotation efforts by radiologists. To tackle this challenge, we introduce FreeTumor, an innovative Generative AI (GAI) framework to enable large-scale tumor synthesis for mitigating data scarcity. Specifically, FreeTumor effectively leverages a combination of limited labeled data and large-scale unlabeled data for tumor synthesis training. Unleashing the power of large-scale data, FreeTumor is capable of synthesizing a large number of realistic tumors on images for augmenting training datasets. To validate the fidelity of synthetic tumors, we engaged 13 board-certified radiologists in a Visual Turing Test to discern between synthetic and real tumors. Rigorous clinician evaluation validates the high quality of our synthetic tumors, as they achieved only 51.1% sensitivity and 60.8% accuracy in distinguishing our synthetic tumors from real ones. Through high-quality tumor synthesis, FreeTumor showcases a notable superiority over state-of-the-art AI methods including various synthesis methods and foundation models. These findings indicate promising prospects of FreeTumor in clinical applications, potentially advancing tumor treatments and improving the survival rates of patients.
We have released the solutions for leaderboard tasks at link.
Our models can be downloaded from link. The datasets are available at link, and processed labels can be found at link. NOTE THAT we are not the authors of these datasets. Although all these datasets are publicly available for academic research, you need to cite the original works as shown in our paper.
Datasets
The labels of trai
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
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