CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification
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arXiv
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| Natura: | Preprint |
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2026
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| author | Dong, Hexin Lin, Yi Zhou, Pengyu Zhao, Fengnian Legasto, Alan Clint Cho, Juno Kim, Dohui Kim, Justin Namuk Kim, Mingeon Kwak, Sunwoo Moyà-Alcover, Gabriel Nguyen, Ky Trung Nguyen, Thanh-Huy Pham, Ha-Hieu Pham, Huy-Hieu Pham, Huy Le Sulake, Nikhileswara Rao Tur-Serrano, Aina Zhang, Ruichi Zu, Ang Flanders, Adam E. Lu, Zhiyong Summers, Ronald M. Lin, Mingquan Chen, Hao Yang, Yuzhe Shih, George Peng, Yifan |
| author_facet | Dong, Hexin Lin, Yi Zhou, Pengyu Zhao, Fengnian Legasto, Alan Clint Cho, Juno Kim, Dohui Kim, Justin Namuk Kim, Mingeon Kwak, Sunwoo Moyà-Alcover, Gabriel Nguyen, Ky Trung Nguyen, Thanh-Huy Pham, Ha-Hieu Pham, Huy-Hieu Pham, Huy Le Sulake, Nikhileswara Rao Tur-Serrano, Aina Zhang, Ruichi Zu, Ang Flanders, Adam E. Lu, Zhiyong Summers, Ronald M. Lin, Mingquan Chen, Hao Yang, Yuzhe Shih, George Peng, Yifan |
| contents | Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from a single institution, failing to capture the prevalence of rare diseases or the appearance of novel findings. To address this, we present the CXR-LT challenge. The first event, CXR-LT 2023, established a large-scale benchmark for long-tailed multi-label CXR classification and identified key challenges in rare disease recognition. CXR-LT 2024 further expanded the label space and introduced a zero-shot task to study generalization to unseen findings. Building on the success of CXR-LT 2023 and 2024, this third iteration of the benchmark introduces a multi-center dataset comprising over 145,000 images from PadChest and NIH Chest X-ray datasets. Additionally, all development and test sets in CXR-LT 2026 are annotated by radiologists, providing a more reliable and clinically grounded evaluation than report-derived labels. The challenge defines two core tasks this year: (1) Robust Multi-Label Classification on 30 known classes and (2) Open-World Generalization to 6 unseen (out-of-distribution) rare disease classes. This paper summarizes the overview of the CXR-LT 2026 challenge. We describe the data collection and annotation procedures, analyze solution strategies adopted by participating teams, and evaluate head-versus-tail performance, calibration, and cross-center generalization gaps. Our results show that vision-language foundation models improve both in-distribution and zero-shot performance, but detecting rare findings under multi-center shift remains challenging. Our study provides a foundation for developing and evaluating AI systems in realistic long-tailed and open-world clinical conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_15555 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification Dong, Hexin Lin, Yi Zhou, Pengyu Zhao, Fengnian Legasto, Alan Clint Cho, Juno Kim, Dohui Kim, Justin Namuk Kim, Mingeon Kwak, Sunwoo Moyà-Alcover, Gabriel Nguyen, Ky Trung Nguyen, Thanh-Huy Pham, Ha-Hieu Pham, Huy-Hieu Pham, Huy Le Sulake, Nikhileswara Rao Tur-Serrano, Aina Zhang, Ruichi Zu, Ang Flanders, Adam E. Lu, Zhiyong Summers, Ronald M. Lin, Mingquan Chen, Hao Yang, Yuzhe Shih, George Peng, Yifan Computer Vision and Pattern Recognition Chest X-ray (CXR) interpretation is hindered by the long-tailed distribution of pathologies and the open-world nature of clinical environments. Existing benchmarks often rely on closed-set classes from a single institution, failing to capture the prevalence of rare diseases or the appearance of novel findings. To address this, we present the CXR-LT challenge. The first event, CXR-LT 2023, established a large-scale benchmark for long-tailed multi-label CXR classification and identified key challenges in rare disease recognition. CXR-LT 2024 further expanded the label space and introduced a zero-shot task to study generalization to unseen findings. Building on the success of CXR-LT 2023 and 2024, this third iteration of the benchmark introduces a multi-center dataset comprising over 145,000 images from PadChest and NIH Chest X-ray datasets. Additionally, all development and test sets in CXR-LT 2026 are annotated by radiologists, providing a more reliable and clinically grounded evaluation than report-derived labels. The challenge defines two core tasks this year: (1) Robust Multi-Label Classification on 30 known classes and (2) Open-World Generalization to 6 unseen (out-of-distribution) rare disease classes. This paper summarizes the overview of the CXR-LT 2026 challenge. We describe the data collection and annotation procedures, analyze solution strategies adopted by participating teams, and evaluate head-versus-tail performance, calibration, and cross-center generalization gaps. Our results show that vision-language foundation models improve both in-distribution and zero-shot performance, but detecting rare findings under multi-center shift remains challenging. Our study provides a foundation for developing and evaluating AI systems in realistic long-tailed and open-world clinical conditions. |
| title | CXR-LT 2026 Challenge: Multi-Center Long-Tailed and Zero Shot Chest X-ray Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.15555 |