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:this https URL

通过创建融合了常见和罕见胸部疾病的新数据集 “LTML-MIMIC-CXR”，我们提出了一种基准方法来解决胸部 X 光片中罕见疾病检测的问题，其中包括了自动标注修正的大型损失重新考虑策略和针对尾部类别负 logit 过抑制的自适应负正则化方法。我们在 LTML-MIMIC-CXR 上的评估显示出罕见疾病检测的显著进展，为稳健的计算机辅助诊断方法提供了基础，以达到在胸部 X 光片中识别一系列胸部疾病的平衡。