  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.
