[Call for Participation] MICCAI 2024 Challenge: CXR-LT

[CXR-LT]

Call for Participation
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We invite you to participate in our MICCAI 2024 challenge, CXR-LT: Long-tailed, multi-label, and zero-shot classification on chest X-rays. This challenge includes three independent tasks and will be conducted through CodaLab.

Challenge website: https://bionlplab.github.io/2024_MICCAI_CXRLT/.

Task 1: Long-tailed classification on a large, noisy test set [CodaLab url];
Task 2: Long-tailed classification on a small, manually annotated test set [CodaLab url];
Task 3: Zero-shot generalization to previous unseen disease findings [CodaLab url].

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Important dates
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• 05/01/2024: Development Phase begins. Training data will be released. Participants can begin making submissions and tracking results on the public leaderboard.
• 08/01/2024: Testing Phase begins. Unlabeled test data will be released to registered participants. The leaderboard will be kept private for this phase.
• 08/04/2024: Testing Phase ends and the challenge is closed.
• 08/15/2024: Top-performing teams will be invited to present at MICCAI 2024.
• 10/10/2024: MICCAI 2024 CXR-LT Challenge event.

Background: Chest radiography, like many diagnostic medical exams, produces a long-tailed distribution of clinical findings; while a small subset of diseases is routinely observed, the vast majority of diseases are relatively rare. This poses a challenge for standard deep learning methods, which exhibit bias toward the most common classes at the expense of the important but rare "tail" classes. Many existing methods have been proposed to tackle this specific type of imbalance, though only recently has attention been given to long-tailed medical image recognition problems. Diagnosis on chest X-rays (CXRs) is also a multi-label problem, as patients often present with multiple disease findings simultaneously; however, only a select few studies incorporate knowledge of label co-occurrence into the learning process. Since most large-scale image classification benchmarks contain single-label images with a mostly balanced distribution of labels, many standard deep learning methods fail to accommodate the class imbalance and co-occurrence problems posed by the long-tailed, multi-label nature of tasks like disease diagnosis on CXRs.

Task: Given a CXR, our challenge includes three tasks, to be held as independent tasks:

• Task 1: long-tailed classification on a large, noisy test set;
• Task 2: long-tailed classification on a small, manually annotated test set;
• Task 3: zero-shot generalization to previously unseen disease findings.

For all tasks, participants will be provided with a large, automatically labeled training set of >250,000 CXR images with 40 binary disease labels. While last year's CXR-LT was a success, we hope that CXR-LT 2024 can provide even further meaningful methodological advances toward clinically realistic long-tailed, multi-label, and zero-shot disease classification on CXR.

Dataset: In the first iteration of CXR-LT held in 2023, we expanded upon the MIMIC-CXR-JPG dataset by enlarging the set of target classes from 14 to 26, generating labels for 12 new rare disease findings by parsing radiology reports. For this year's version of CXR-LT, we extract labels for an additional 19 rare disease findings (for a total of 377,110 CXR images, each with 45 disease labels).

Challenge: This challenge is hosted in conjunction with the MICCAI 2024. We will invite participants to submit their solutions for potential presentations at the MICCAI 2024 CXR-LT challenge. Additionally, we plan to coordinate a publication summarizing the challenge results, with invitations extended to the top-performing teams to serve as coauthors. We intend to select the top 3 teams for oral presentations at the MICCAI 2024 challenge in Morocco.

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Steering Committee
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• George Shih, MD | Weill Cornell Medicine
• Zhiyong Lu, PhD, FACMI | NIH
• Ronald M. Summers, MD, PhD | NIH
• Leo Anthony Celi, MD, MPH, MSc | MIT/Harvard
• Adam Flanders, MD | Thomas Jefferson University

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Organizing Committee
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• Mingquan Lin, PhD | University of Minnesota
• Gregory Holste | The University of Texas at Austin
• Song Wang | The University of Texas at Austin
• Yiliang Zhou | Weill Cornell Medicine
• Hao Chen, PhD | Hong Kong University of Science and Technology
• Atlas Wang, PhD | The University of Texas at Austin
• Yifan Peng (Chair), PhD | Weill Cornell Medicine

For any questions, please contact cxrltchal...@gmail.com.

Best regards,

CXR-LT Organizers