[Call for Participation] ICCV 2023 CVAMD Shared Task: CXR-LT
CXR-LT CVAMD 2023
Call for Participation
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We invite you to participate in our competition, CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays. This is the shared task of the ICCV 2023 (https://iccv2023.thecvf.com/) workshop on Computer Vision for Automated Medical Diagnosis (CVAMD) (https://cvamd2023.github.io/). This competition will be conducted through CodaLab.
Competition website: https://bionlplab.github.io/2023_ICCV_CVAMD/
CodaLab website: https://codalab.lisn.upsaclay.fr/competitions/12599
Important dates
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- 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.
Background: Many real-world problems, including diagnostic medical imaging exams, are “long-tailed” – there are a few common findings followed by more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple disease findings simultaneously. This is distinct from most large-scale image classification benchmarks, where each image only belongs to one label and the distribution of labels is relatively balanced. 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.
Task: Given a CXR, detect all clinical findings. If no findings are present, predict "No Finding" (with the exception that "No Finding" can co-occur with "Support Devices"). To do this, you will train multi-label thorax disease classifiers on the provided labeled training data.
Dataset: This challenge will use an expanded version of MIMIC-CXR-JPG, a large benchmark dataset for automated thorax disease classification (https://physionet.org/content/mimic-cxr-jpg/2.0.0/). 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).
Workshop: This competition is hosted in conjunction with the ICCV CVAMD 2023. Upon completion of the competition, we will invite participants to submit their solutions for potential presentation at CVAMD 2023 and publication in the ICCV 2023 workshop proceedings. We intend to accept 5-6 papers for publication and select 3 of the accepted papers for oral presentation at CVAMD in Paris.
Steering Committee
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- Leo Anthony Celi, MD, MPH, MSc | MIT/Harvard
- Zhiyong Lu, PhD, FACMI | NIH
- George Shih, MD | Weill Cornell Medicine
- Ronald M. Summers, MD, PhD | NIH
Organizing Committee
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- Atlas Wang (chair) | The University of Texas at Austin
- Yifan Peng (chair) | Weill Cornell Medicine
- Gregory Holste | The University of Texas at Austin
- Ajay Jaiswal | The University of Texas at Austin
- Mingquan Lin | Weill Cornell Medicine
- Song Wang | The University of Texas at Austin
- Yuzhe Yang | MIT
This competition is supported in part by the Artificial Intelligence Journal (AIJ). For any questions, please contact cxr.lt.comp...@gmail.com.
Best,
CXR-LT Organizers