Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification.

在医学图像识别中，我们提出了一个长尾多标签疾病分类的问题，并通过一个公开的挑战(CXR-LT)提供了一个大规模数据集来解决这个问题。我们总结了表现最好的解决方案的共同主题，并提出了对于长尾多标签医学图像分类的实用建议，最后，我们利用这些观察结果提出了基于视觉语言模型的少标签和零标签疾病分类的前进方向。

基于胸部X射线的长尾、多标签疾病分类研究概述：CXR-LT挑战的综述