Announcing CXR-LT, a competition for long-tailed disease classification on chest X-rays

News from: CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays v1.0.0.

June 21, 2023

We are pleased to announce CXR-LT, a competition on Multi-Label Long-Tailed Classification on Chest X-Rays. Many real-world problems, including diagnostic medical imaging exams, are “long-tailed”: there are a few common findings followed by more relatively rare conditions. This competition will provide a challenging large-scale multi-label long-tailed learning task on chest X-rays (CXRs), encouraging community engagement with this emerging interdisciplinary topic.

CXR-LT is organized as a shared task for the workshop on Computer Vision for Automated Medical Diagnosis (CVAMD) held in association with the International Conference on Computer Vision (ICCV) 2023. Participants will be invited to submit their solutions for publication presentation at CVAMD 2023 and publication in the ICCV 2023 workshop proceedings.

The challenge uses an expanded version of MIMIC-CXR-JPG v2.0.0, a large benchmark dataset for automated thorax disease classification. Each CXR study in the dataset was labeled with 12 newly added disease findings extracted from the associated radiology reports. The resulting long-tailed (LT) dataset contains 377,110 CXRs, each labeled with at least one of 26 clinical findings (including a "No Finding" class).

Important dates

05/01/2023: Development Phase begins. Participants can begin making submissions and tracking results on the public leaderboard.
07/14/2023: Testing Phase begins. Unlabeled test data will be released to registered participants. The leaderboard will be kept private for this phase.
07/17/2023: Competition ends. Participants are invited to submit their solutions as 8-page papers to ICCV CVAMD 2023!
07/28/2023: ICCV CVAMD 2023 submission deadline. (Competition participants may receive an extension if needed.)
08/11/2023: ICCV CVAMD 2023 acceptance notification.
10/06/2023: ICCV CVAMD 2023 workshop.

This competition is supported in part by the Artificial Intelligence Journal (AIJ). For any questions, please contact

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