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In recent years, convolutional neural networks (CNNs) have been successfullyimplemented to various image recognition applications, such as medical imageanalysis, object detection, and image segmentation. Many studies andapplications have been working on improving the performance of CNN algorithmsand models. The strategies that aim to improve the performance of CNNs can begrouped into three major approaches: (1) deeper and wider network architecture,(2) automatic architecture search, and (3) convolutional attention block.Unlike approaches (1) and (2), the convolutional attention block approach ismore flexible with lower cost. It enhances the CNN performance by extractingmore efficient features. However, the existing attention blocks focus onenhancing the significant features, which lose some potential features in theuncertainty information. Inspired by the test time augmentation and test-timedropout approaches, we developed a novel convolutional uncertainty attentionblock (CUAB) that can leverage the uncertainty information to improve CNN-basedmodels. The proposed module discovers potential information from the uncertainregions on feature maps in computer vision tasks. It is a flexible functionalattention block that can be applied to any position in the convolutional blockin CNN models. We evaluated the CUAB with notable backbone models, ResNet andResNeXt, on a medical image segmentation task. The CUAB achieved a dice scoreof 73% and 84% in pneumonia and pneumothorax segmentation, respectively,thereby outperforming the original model and other notable attentionapproaches. The results demonstrated that the CUAB can efficiently utilize theuncertainty information to improve the model performance.

CUAB: Convolutional Uncertainty Attention Block Enhanced the Chest X-ray Image Analysis