Computer-aided diagnosis (CAD) technology can assist clinicians in evaluating liver lesions and intervening with treatment in time. Although CAD technology has advanced in recent years, the application scope of existing datasets remains relatively limited, typically supporting only single tasks, which has somewhat constrained the development of CAD technology. To address the above limitation, in this paper, we construct a multi-task liver dataset (LiMT) used for liver and tumor segmentation, multi-label lesion classification, and lesion detection based on arterial phase-enhanced computed tomography (CT), potentially providing an exploratory solution that is able to explore the correlation between tasks and does not need to worry about the heterogeneity between task-specific datasets during training. The dataset includes CT volumes from 150 different cases, comprising four types of liver diseases as well as normal cases. Each volume has been carefully annotated and calibrated by experienced clinicians. This public multi-task dataset may become a valuable resource for the medical imaging research community in the future. In addition, this paper not only provides relevant baseline experimental results but also reviews existing datasets and methods related to liver-related tasks. Our dataset is available at https://drive.google.com/drive/folders/1l9HRK13uaOQTNShf5pwgSz3OTanWjkag?usp=sharing.
@article{arxiv.2511.19889,
title = {LiMT: A Multi-task Liver Image Benchmark Dataset},
author = {Zhe Liu and Kai Han and Siqi Ma and Yan Zhu and Jun Chen and Chongwen Lyu and Xinyi Qiu and Chengxuan Qian and Yuqing Song and Yi Liu and Liyuan Tian and Yang Ji and Yuefeng Li},
journal= {arXiv preprint arXiv:2511.19889},
year = {2025}
}