The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality interaction. Our results on an external validation set of chest x-rays suggest that LRCLR identifies significant local image regions and provides meaningful interpretation against radiology text while improving zero-shot performance on several chest x-ray medical findings.

翻译：基于深度学习的放射影像分析方法的普及，催生了对专家标注放射学数据的大量需求。近期自监督框架通过关联放射学报告获取监督信号，缓解了对专家标注的依赖。然而，此类框架难以区分医学图像中不同病理之间的细微差异。此外，多数框架未能提供图像区域与文本之间的解释，导致放射科医师难以评估模型预测结果。本文提出局部区域对比学习（LRCLR），一种灵活的微调框架，该框架新增了显著图像区域选择层与跨模态交互层。我们在外部验证集上的胸部X光片实验表明，LRCLR能够识别关键局部图像区域，并提供与放射学文本有意义的解释，同时提升多项胸部X光医学发现的零样本性能。