The paucity of high-quality medical imaging datasets could be mitigated by machine
learning models that generate compositionally diverse images that faithfully represent …

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep
learning models capable of interpreting and generating both textual and imaging data. Such …

Deep learning and advancements in computer vision offer significant potential for analyzing
medical images resulting in better healthcare and improved patient outcomes. Currently, the …

Over the last decade, artificial intelligence (AI) has made an enormous impact on a wide
range of fields, including science, engineering, informatics, finance, and transportation. In …

Evaluation of MR reconstruction methods is challenged by the need for image quality (IQ)
metrics which correlate strongly with radiologist-perceived IQ. We explore Deep Feature …

Recently, numerous pancreas segmentation methods have achieved promising
performance on local single-source datasets. However, these methods don't adequately …

Background and objectives: Cerebrovascular segmentation plays a crucial role in guiding
the diagnosis and treatment of cerebrovascular diseases. With the rapid advancements in …

Objective: Recent deep learning techniques hold promise to enable IMU-driven kinetic
assessment; however, they require large extents of ground reaction force (GRF) data to …

Imaging evaluation of articular cartilage plays an important role in clinical decision making.
Many new and evolving surgical and nonsurgical treatment options are available for various …

The accurate segmentation of individual muscles is essential for quantitative MRI analysis of
thigh images. Deep learning methods have achieved state‐of‐the‐art results in …