Development of lung segmentation method in x-ray images of children based on TransResUNet.

Background: Chest x-ray (CXR) is widely applied for the detection and diagnosis of children's lung diseases. Lung field segmentation in digital CXR images is a key section of many computer-aided diagnosis systems.

Objective: In this study, we propose a method based on deep learning to improve the lung segmentation quality and accuracy of children's multi-center CXR images.

Variational deep embedding-based active learning for the diagnosis of pneumonia.

Machine learning works similar to the way humans train their brains. In general, previous experiences prepared the brain by firing specific nerve cells in the brain and increasing the weight of the links between them. Machine learning also completes the classification task by constantly changing the weights in the model through training on the training set.

Warfarin treatment among Finnish patients with atrial fibrillation: retrospective registry study based on primary healthcare data.

Objective: To assess the frequency of warfarin use, the achieved international normalised ratio (INR) balance among warfarin users and the primary healthcare outpatient costs of patients with atrial fibrillation (AF).

Design: Retrospective, non-interventional registry study.

Setting: Primary healthcare.