The resulting MIMIC-CXR-LT dataset contains 19 classes, of which 10 are head classes, 6 are medium classes, and 3 are tail classes. MIMIC-CXR-LT contains ...

28 сент. 2023 г. · This competition will provide a challenging large-scale multi-label long-tailed learning task on chest X-rays (CXRs), encouraging community engagement.

The current state-of-the-art on MIMIC-CXR-LT is Decoupling (cRT). See a full comparison of 15 papers with code.

12 июн. 2023 г. · This competition will provide a challenging large-scale multi-label long-tailed learning task on chest X-rays (CXRs), encouraging community engagement.

This task will evaluate a model's ability to perform "in-distribution" long-tailed, multi-label disease classification on CXRs when evaluated on a large test ...

Run all MIMIC-CXR-LT experiments: bash run_mimic-cxr-lt_experiments.sh (first changing the --data_dir argument to your MIMIC-CXR path). Run all NIH-CXR-LT ...

Chest X-rays (CXRs) often display various diseases with disparate class frequencies, leading to a long-tailed, multi-label data distribution.

We invite you to participate in our MICCAI 2024 challenge, CXR-LT: Long-tailed, multi-label, and zero-shot classification on chest X-rays.

[DALI 2022] "Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study" by Gregory Holste, Song Wang, Ziyu Jiang, Thomas C. Shen, ...

In the first iteration of CXR-LT held in 2023, we expanded upon the MIMIC-CXR dataset by enlarging the set of target classes from 14 to 26, generating labels ...