真实世界多模态纵向肺癌数据集
包含1365名肺癌患者的多中心、多模态纵向数据集，涵盖全切片图像、CT、PET、临床数据和转录组学，支持多模态融合研究。
基本信息
资源简介
真实世界多模态纵向肺癌数据集
【数据集背景】
本数据集源站标题为《ritacmendes/MMIST-LUNG》。源站首次发布：2026-08-11。
【数据内容】
源站原始描述：Real-World Multi-Model and Longitudinal Lung Cancer Dataset This repository contains the benchmark code used in the paper “Real-World Multi-Modal and Longitudinal Lung Cancer Dataset,” presented at the ECCV DCA-MI Workshop, as well as the MMIST-LUNG features and folds. The dataset is hosted on the MMIST website and its statistics and can be accessed here. # Real-World Multi-Modal and Longitudinal Lung Cancer Dataset Abstract Multi-modal learning has demonstrated strong potential in medical a…
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://github.com/ritacmendes/MMIST-LUNG
【使用注意】
· 源站最后修改于 2026-09-17，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录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
git clone https://github.com/ritacmendes/MMIST-LUNG.git
curl -L -o repo.zip https://github.com/ritacmendes/MMIST-LUNG/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
This repository contains the benchmark code used in the paper “Real-World Multi-Modal and Longitudinal Lung Cancer Dataset,” presented at the ECCV DCA-MI Workshop, as well as the MMIST-LUNG features and folds.
The dataset is hosted on the MMIST website and its statistics and can be accessed here.
Real-World Multi-Modal and Longitudinal Lung Cancer Dataset
Abstract Multi-modal learning has demonstrated strong potential in medical applications by integrating heterogeneous data sources such as medical imaging, clinical records, and genomics to improve predictive performance and support clinical decision-making. However, advances in this area are often constrained by two key challenges: the limited availability of well-curated, ready-to-use datasets that accurately reflect real-world conditions, where medical data are frequently collected inconsistently and are often incomplete; and the inherent difficulty of integrating heterogeneous data modalities. In this work, we introduce a newly curated multi-center, multi-modal, and longitudinal dataset designed to support the evaluation of a wide range of learning pipelines under realistic conditions. The dataset comprises a total of 1,365 lung cancer patients and has three imaging modalities (whole-slide images, CT scans, and PET scans), structured clinical data, transcriptomic, and longitudinal follow-up and treatment information. For each imaging modality the dataset contains more than one instance. Moreover, the dataset exhibits substantial and non-uniform missingness across modalities, making it well-suited for studying robust multi-modal fusion strategies. We further provide both uni-modal and multi-modal benchmarks on the task of 12-month overall survival prediction, disease-specific survival, as well as longitudinal benchmark of hazard prediction under severe missing data.
Our results show that, despite high levels of missingness, integrating complementary modalities consistently improves predictive performance over uni-modal approaches, highlighting the value of multi-modal fusion in realistic clinical settings.
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
公开数据往往缺乏完整的病理回证与专家标注。千方医数集依托陕川双基地与超十家三甲医院直连，提供完全合规、去标识化、支持注册级质控的专病数据集交易服务。
