论文标题
CHEXRELNET：一种用于跟踪胸部X射线之间纵向关系的解剖学模型
CheXRelNet: An Anatomy-Aware Model for Tracking Longitudinal Relationships between Chest X-Rays
论文作者
论文摘要
尽管在利用深度学习来自动化胸部X光片解释和疾病诊断任务方面取得了进展，但顺序胸部X射线（CXR）之间的变化受到了有限的关注。监测通过胸部成像可视化的病理的进展在解剖运动估计和图像注册中构成了几个挑战，即在空间上对齐这两个图像并在变化检测中对时间动态进行建模。在这项工作中，我们提出了Chexrelnet，这是一种神经模型，可以跟踪两个CXR之间的纵向病理改变关系。 Chexrelnet结合了局部和全球视觉特征，利用图像间和图像内的解剖信息，并学习解剖区域属性之间的依赖性，以准确预测一对CXR的疾病变化。胸部成像素数据集的实验结果显示，与基线相比，下游性能提高。代码可从https://github.com/plan-lab/chexrelnet获得
Despite the progress in utilizing deep learning to automate chest radiograph interpretation and disease diagnosis tasks, change between sequential Chest X-rays (CXRs) has received limited attention. Monitoring the progression of pathologies that are visualized through chest imaging poses several challenges in anatomical motion estimation and image registration, i.e., spatially aligning the two images and modeling temporal dynamics in change detection. In this work, we propose CheXRelNet, a neural model that can track longitudinal pathology change relations between two CXRs. CheXRelNet incorporates local and global visual features, utilizes inter-image and intra-image anatomical information, and learns dependencies between anatomical region attributes, to accurately predict disease change for a pair of CXRs. Experimental results on the Chest ImaGenome dataset show increased downstream performance compared to baselines. Code is available at https://github.com/PLAN-Lab/ChexRelNet
