LTCXNet提升罕见胸片病灶检测，兼顾准确与公平性
LTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed Multi-Label Classification and Fairness Challenges
- 融合ConvNeXt与ML-Decoder，结合数据增强和集成学习
- 对罕见病如气腹、纵隔气肿检测率提升79%和48%
- 关注模型公平性，避免不同人群间诊断偏差
胸部X光片常呈现多种疾病，且各类别频率差异显著，形成长尾多标签分布。针对此问题，我们采用从MIMIC-CXR数据集衍生的剪枝版MIMIC-CXR-LT数据集，构建了专门模拟长尾多标签场景的基准。提出LTCXNet框架，整合ConvNeXt、ML-Decoder及策略性数据增强，并引入集成方法。实验表明，LTCXNet在所有类别上均提升诊断性能，尤其使罕见病‘Pneumoperitoneum’和‘Pneumomediastinum’的检测率分别提高79%和48%。此外，研究还评估了模型公平性，发现部分虽提升准确率的方法可能对不同人口群体造成负面影响。本工作推动了医学影像中长尾多标签分布的理解与管理，助力更公平高效的诊断工具发展。
原文摘要 · Abstract (English)
Chest X-rays (CXRs) often display various diseases with disparate class frequencies, leading to a long-tailed, multi-label data distribution. In response to this challenge, we explore the Pruned MIMIC-CXR-LT dataset, a curated collection derived from the MIMIC-CXR dataset, specifically designed to represent a long-tailed and multi-label data scenario. We introduce LTCXNet, a novel framework that integrates the ConvNeXt model, ML-Decoder, and strategic data augmentation, further enhanced by an ensemble approach. We demonstrate that LTCXNet improves the performance of CXR interpretation across all classes, especially enhancing detection in rarer classes like `Pneumoperitoneum' and `Pneumomediastinum' by 79\% and 48\%, respectively. Beyond performance metrics, our research extends into evaluating fairness, highlighting that some methods, while improving model accuracy, could inadvertently affect fairness across different demographic groups negatively. This work contributes to advancing the understanding and management of long-tailed, multi-label data distributions in medical imaging, paving the way for more equitable and effective diagnostic tools.
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