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.