A Novel Attention Mechanism Using Anatomical Prior Probability Maps for Thoracic Disease Classification from X-Ray Images

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. Based on this knowledge, 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. 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 0.8427. Regarding disease localization, the proposed method shows competitive performance compared to state-of-the-art methods, achieving an accuracy of 61% with an Intersection over Union (IoU) threshold of 0.3. The proposed method can also be generalized to other medical image-based disease classification and localization tasks where the probability of occurrence of the lesion is dependent on specific anatomical sites.