Liver fibrosis represents a significant global health burden, necessitating accurate staging for effective clinical management. This report introduces the LiQA (Liver Fibrosis Quantification and Analysis) dataset, established as part of the CARE 2024 challenge. Comprising 440 patients with multi-phase, multi-center MRI scans, the dataset is curated to benchmark algorithms for Liver Segmentation (LiSeg) and Liver Fibrosis Staging (LiFS) under complex real-world conditions, including domain shifts, missing modalities, and spatial misalignment. We further describe the challenge's top-performing methodology, which integrates a semi-supervised learning framework with external data for robust segmentation, and utilizes a multi-view consensus approach with Class Activation Map (CAM)-based regularization for staging. Evaluation of this baseline demonstrates that leveraging multi-source data and anatomical constraints significantly enhances model robustness in clinical settings.
Cite
@article{arxiv.2512.07651,
title = {Liver Fibrosis Quantification and Analysis: The LiQA Dataset and Baseline Method},
author = {Yuanye Liu and Hanxiao Zhang and Jiyao Liu and Nannan Shi and Yuxin Shi and Arif Mahmood and Murtaza Taj and Xiahai Zhuang},
journal= {arXiv preprint arXiv:2512.07651},
year = {2025}
}