Objective: Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known that different thoracic disease lesions are more likely to occur in specific anatomical regions compared to others. This article aims to incorporate this disease and region-dependent prior probability distribution within a deep learning framework. Methods: We present the ThoraX-PriorNet, a novel attention-based CNN model for thoracic disease classification. We first estimate a disease-dependent spatial probability, i.e., an anatomical prior, that indicates the probability of occurrence of a disease in a specific region in a chest X-ray image. Next, we develop a novel attention-based classification model that combines information from the estimated anatomical prior and automatically extracted chest region of interest (ROI) masks to provide attention to the feature maps generated from a deep convolution network. Unlike previous works that utilize various self-attention mechanisms, the proposed method leverages the extracted chest ROI masks along with the probabilistic anatomical prior information, which selects the region of interest for different diseases to provide attention. Results: The proposed method shows superior performance in disease classification on the NIH ChestX-ray14 dataset compared to existing state-of-the-art methods while reaching an area under the ROC curve (%AUC) of 84.67. Regarding disease localization, the anatomy prior attention method shows competitive performance compared to state-of-the-art methods, achieving an accuracy of 0.80, 0.63, 0.49, 0.33, 0.28, 0.21, and 0.04 with an Intersection over Union (IoU) threshold of 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, and 0.7, respectively.

翻译：目的：基于医学图像的计算机辅助疾病诊断与预后是一个快速发展的领域。研究人员已开发出多种卷积神经网络架构用于胸部X光图像的疾病分类与定位。已知不同胸部疾病病灶更可能出现在特定解剖区域。本文旨在将这种与疾病和区域相关的先验概率分布融入深度学习框架。方法：我们提出了ThoraX-PriorNet，一种新型基于注意力的卷积神经网络模型，用于胸部疾病分类。首先估计疾病相关的空间概率（即解剖先验），该概率指示胸部X光图像中特定区域发生某种疾病的概率。接着开发了一种新颖的基于注意力的分类模型，该模型结合了估计的解剖先验与自动提取的胸部感兴趣区域掩模信息，为深度卷积网络生成的特征图提供注意力机制。与之前利用各种自注意力机制的工作不同，所提方法利用提取的胸部感兴趣区域掩模及概率解剖先验信息，为不同疾病选择关注区域以提供注意力。结果：所提方法在NIH ChestX-ray14数据集上的疾病分类表现优于现有最先进方法，ROC曲线下面积达84.67%。在疾病定位方面，基于解剖先验的注意力方法在交并比阈值为0.1、0.2、0.3、0.4、0.5、0.6、0.7时分别达到0.80、0.63、0.49、0.33、0.28、0.21、0.04的准确率，与最先进方法相比具有竞争力。