CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray

The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance. Building on the success of CXR-LT 2023, the CXR-LT 2024 expands the dataset to 377,110 chest X-rays (CXRs) and 45 disease labels, including 19 new rare disease findings. It also introduces a new focus on zero-shot learning to address limitations identified in the previous event. Specifically, CXR-LT 2024 features three tasks: (i) long-tailed classification on a large, noisy test set, (ii) long-tailed classification on a manually annotated "gold standard" subset, and (iii) zero-shot generalization to five previously unseen disease findings. This paper provides an overview of CXR-LT 2024, detailing the data curation process and consolidating state-of-the-art solutions, including the use of multimodal models for rare disease detection, advanced generative approaches to handle noisy labels, and zero-shot learning strategies for unseen diseases. Additionally, the expanded dataset enhances disease coverage to better represent real-world clinical settings, offering a valuable resource for future research. By synthesizing the insights and innovations of participating teams, we aim to advance the development of clinically realistic and generalizable diagnostic models for chest radiography.

@article{lin2025_2506.07984,
  title={ CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray },
  author={ Mingquan Lin and Gregory Holste and Song Wang and Yiliang Zhou and Yishu Wei and Imon Banerjee and Pengyi Chen and Tianjie Dai and Yuexi Du and Nicha C. Dvornek and Yuyan Ge and Zuowei Guo and Shouhei Hanaoka and Dongkyun Kim and Pablo Messina and Yang Lu and Denis Parra and Donghyun Son and Álvaro Soto and Aisha Urooj and René Vidal and Yosuke Yamagishi and Zefan Yang and Ruichi Zhang and Yang Zhou and Leo Anthony Celi and Ronald M. Summers and Zhiyong Lu and Hao Chen and Adam Flanders and George Shih and Zhangyang Wang and Yifan Peng },
  journal={arXiv preprint arXiv:2506.07984},
  year={ 2025 }
}