CXR-LT: 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. 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, 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 project contains labels for the CXR-LT 2023
competition dataset, containing 377,110 CXRs from 26 classes, and a related
subset used in the MICCAI 2023 paper, "How Does Pruning Impact Multi-Label
Long-Tailed Learning?" containing 257,018 frontal CXRs from 19 classes.