  Chest X-rays (CXR) often reveal rare diseases, demanding precise diagnosis.
However, current computer-aided diagnosis (CAD) methods focus on common
diseases, leading to inadequate detection of rare conditions due to the absence
of comprehensive datasets. To overcome this, we present a novel benchmark for
long-tailed multi-label classification in CXRs, encapsulating both common and
rare thoracic diseases. Our approach includes developing the "LTML-MIMIC-CXR"
dataset, an augmentation of MIMIC-CXR with 26 additional rare diseases. We
propose a baseline method for this classification challenge, integrating
adaptive negative regularization to address negative logits' over-suppression
in tail classes, and a large loss reconsideration strategy for correcting noisy
labels from automated annotations. Our evaluation on LTML-MIMIC-CXR
demonstrates significant advancements in rare disease detection. This work
establishes a foundation for robust CAD methods, achieving a balance in
identifying a spectrum of thoracic diseases in CXRs. Access to our code and
dataset is provided at:https://github.com/laihaoran/LTML-MIMIC-CXR.
