CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908400358522880 |
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| author | Lin, Mingquan Holste, Gregory Wang, Song Zhou, Yiliang Wei, Yishu Banerjee, Imon Chen, Pengyi Dai, Tianjie Du, Yuexi Dvornek, Nicha C. Ge, Yuyan Guo, Zuowei Hanaoka, Shouhei Kim, Dongkyun Messina, Pablo Lu, Yang Parra, Denis Son, Donghyun Soto, Álvaro Urooj, Aisha Vidal, René Yamagishi, Yosuke Yang, Zefan Zhang, Ruichi Zhou, Yang Celi, Leo Anthony Summers, Ronald M. Lu, Zhiyong Chen, Hao Flanders, Adam Shih, George Wang, Zhangyang Peng, Yifan |
| author_facet | Lin, Mingquan Holste, Gregory Wang, Song Zhou, Yiliang Wei, Yishu Banerjee, Imon Chen, Pengyi Dai, Tianjie Du, Yuexi Dvornek, Nicha C. Ge, Yuyan Guo, Zuowei Hanaoka, Shouhei Kim, Dongkyun Messina, Pablo Lu, Yang Parra, Denis Son, Donghyun Soto, Álvaro Urooj, Aisha Vidal, René Yamagishi, Yosuke Yang, Zefan Zhang, Ruichi Zhou, Yang Celi, Leo Anthony Summers, Ronald M. Lu, Zhiyong Chen, Hao Flanders, Adam Shih, George Wang, Zhangyang Peng, Yifan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray Lin, Mingquan Holste, Gregory Wang, Song Zhou, Yiliang Wei, Yishu Banerjee, Imon Chen, Pengyi Dai, Tianjie Du, Yuexi Dvornek, Nicha C. Ge, Yuyan Guo, Zuowei Hanaoka, Shouhei Kim, Dongkyun Messina, Pablo Lu, Yang Parra, Denis Son, Donghyun Soto, Álvaro Urooj, Aisha Vidal, René Yamagishi, Yosuke Yang, Zefan Zhang, Ruichi Zhou, Yang Celi, Leo Anthony Summers, Ronald M. Lu, Zhiyong Chen, Hao Flanders, Adam Shih, George Wang, Zhangyang Peng, Yifan Computer Vision and Pattern Recognition Machine Learning 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. |
| title | CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2506.07984 |