ICCV CVAMD 2023 Shared TaskCXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays |
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Location: Paris, France Time: TBD, October 2-3, 2023 |
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. While researchers have begun to study the problem of long-tailed learning in medical image recognition [1-3], few have studied its interplay with label co-occurrence. 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.
This competition is hosted in conjunction with the ICCV 2023 workshop, Computer Vision for Automated Medical Diagnosis (CVAMD). Upon completion of the competition, we will invite participants to submit their solutions for porential 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. For more information about CVAMD, see https://cvamd2023.github.io/.
This challenge will use an expanded version of MIMIC-CXR-JPG [4], a large benchmark dataset for automated thorax disease classification. Following [1], each CXR study in the dataset is labeled with a total of 12 new rare disease findings extracted from 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).
Given a CXR, detect all pathologies present (or predict “No Finding” if none present). To do this, you will train multi-label thorax disease classifiers on the provided labeled training data.
Models will be evaluated on the provided testing set using “macro-averaged” mean Average Precision (mAP).
The competition will be conducted through the CodaLab platform.
5/1/2023. Training data release and competition begins
7/21/2023. Test data release and final evaluation begins
7/28/2023. Workshop paper submissions are due
8/11/2023. Paper acceptance notification
8/25/2023. Camera-ready papers due
10/6/2023. ICCV CVAMD workshop
Please contact cxr.lt.competition.2023@gmail.com if you have any questions. This webpage template is by courtesy of the awesome Georgia.
This comeptition is sponsored in part by the Artifical Intelligence Journal (AIJ).