A Low-Field MRI Dataset For Spatiotemporal Analysis of Developing Brain.

Recently, imaging investigation of brain development has increasingly captured the attention of researchers and clinicians in an attempt to understand the link between the brain and behavioral changes. Although high-field MR imaging of infants is feasible, the necessary customizations have limited its accessibility, affordability, and reproducibility. Low-field MR, as an emerging solution for scrutinizing developing brain, has exhibited its unique advantages in safety, portability, and cost-effectiveness.

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.

Multiple-kernel, multiple-instance similarity features for efficient visual object detection.

We propose to use the similarity between the sample instance and a number of exemplars as features in visual object detection. Concepts from multiple-kernel learning and multiple-instance learning are incorporated into our scheme at the feature level by properly calculating the similarity. The similarity between two instances can be measured by various metrics and by using the information from various sources, which mimics the use of multiple kernels for kernel machines.