The global challenge in chest radiograph X-ray (CXR) abnormalities often being misdiagnosed is primarily associated with perceptual errors, where healthcare providers struggle to accurately identify the location of abnormalities, rather than misclassification errors. We currently address this problem through disease-specific segmentation models. Unfortunately, these models cannot be released in the field due to their lack of generalizability across all thoracic diseases. A binary model tends to perform poorly when it encounters a disease that isn't represented in the dataset. We present CheX-nomaly: a binary localization U-net model that leverages transfer learning techniques with the incorporation of an innovative contrastive learning approach. Trained on the VinDr-CXR dataset, which encompasses 14 distinct diseases in addition to 'no finding' cases, my model achieves generalizability across these 14 diseases and others it has not seen before. We show that we can significantly improve the generalizability of an abnormality localization model by incorporating a contrastive learning method and dissociating the bounding boxes with its disease class. We also introduce a new loss technique to apply to enhance the U-nets performance on bounding box segmentation. By introducing CheX-nomaly, we offer a promising solution to enhance the precision of chest disease diagnosis, with a specific focus on reducing the significant number of perceptual errors in healthcare.

翻译：胸部放射线X光（CXR）异常常被误诊的全球性挑战，主要与感知错误相关——即医疗工作者难以准确定位异常部位，而非分类错误。目前我们通过疾病特异性分割模型来解决这一问题。遗憾的是，由于这些模型缺乏对所有胸部疾病的泛化能力，无法在实际环境中部署。二元模型在遇到数据集中未包含的疾病时往往表现不佳。我们提出CheX-nomaly：一种融合迁移学习技术与创新性对比学习方法的二元定位U-net模型。该模型基于包含14种不同疾病及"无异常"病例的VinDr-CXR数据集进行训练，实现了对这14种疾病及其他未见疾病的泛化能力。研究表明，通过引入对比学习方法并将边界框与其疾病类别解耦，可显著提升异常定位模型的泛化性能。我们还提出了一种新型损失函数，用于增强U-net在边界框分割任务上的表现。通过推出CheX-nomaly，我们为提升胸部疾病诊断精度提供了可行方案，尤其聚焦于减少医疗场景中大量存在的感知错误。