Abstract
Despite the advancement of deep learning-based computer-aided diagnosis (CAD) methods for pneumonia from adult chest x-ray (CXR) images, the performance of CAD methods applied to pediatric images remains suboptimal, mainly due to the lack of large-scale annotated pediatric imaging datasets. Establishing a proper framework to leverage existing adult large-scale CXR datasets can thus enhance pediatric pneumonia detection performance. In this paper, we propose a three-branch parallel path learning-based framework that utilizes both adult and pediatric datasets to improve the performance of deep learning models on pediatric test datasets. The paths are trained with pediatric only, adult only, and both types of CXRs, respectively. Our proposed framework utilizes the multi-positive contrastive loss to cluster the classwise embeddings and the embedding similarity loss among these three parallel paths to make the classwise embeddings as close as possible to reduce the effect of domain shift. Experimental evaluations on open-access adult and pediatric CXR datasets show that the proposed method achieves a superior AUROC score of 0.8464 compared to 0.8348 obtained using the conventional approach of join training on both datasets. The proposed approach thus paves the way for generalized CAD models that are effective for both adult and pediatric age groups.
Abstract (translated)
尽管基于深度学习的计算机辅助诊断（CAD）方法在成人胸部X光（CXR）图像上取得了进步，但是应用于儿童图像的CAD方法的表现仍然较低，主要原因是缺乏大型注释的儿童成像数据集。因此，建立一个适当的框架来利用现有的大规模成人CXR数据集可以提高儿童肺炎检测性能。在本文中，我们提出了一个三分支并行路径学习框架，该框架利用了成人和儿童数据集，以提高儿童测试数据上深度学习模型的性能。这些路径分别通过儿童仅、成人仅和两者的组合进行训练。我们提出的框架利用多正比对比损失来对这三个并行的路径进行聚类，利用嵌入相似性损失来使这三个路径的类内嵌入尽可能接近，以减少领域漂移的影响。在公开访问的成人及儿童CXR数据集上进行实验评估，结果显示，与传统方法将两个数据集的数据分别合并训练相比，所提出的 method 实现了 superior 的 AUROC 分数为 0.8464，而传统方法的 AUROC 分数为 0.8348。因此，所提出的框架为通用的 CAD 模型铺平了道路，这些模型对成人和儿童年龄段都有效。
URL
https://arxiv.org/abs/2404.12958
