Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models (> 10% in F1-score) and increases the model's interpretability.

翻译：切斯特X射线因其非侵入性而成为最常见的医学诊断之一。胸部X射线图像的数量急剧上升,但阅读胸部X射线仍由放射学家手工进行,造成大量烧伤和延误。传统上,放射作为放射学的子领域,可以从医疗图像中提取大量数量特征,表明其在深层次学习时代前促进医学成像诊断的潜力。随着深层次学习的兴起,胸部X射线诊断的深神经网络解释性仍然不透明。在本研究中,我们提出了一个新的框架,利用放射特征和对比学习在胸部X射线中检测肺炎。RSNA肺部探测挑战数据集的实验显示,我们的模型取得了优异于几种最先进的模型(在F1-分层中为10%)的结果,并增加了模型的可解释性。