I lead the Computer-assisted Applications in Medicine (CAiM) research group, where we work on developing AI-based image analysis and computer vision techniques for medical images. Under a unified umbrella, several PhD students and many master's students work in CAiM on multiple related projects from X-ray computed tomography (CT) to magnetic resonance (MR) images. A major focus and a unique expertise of our group is on novel ultrasound imaging techniques. We also develop fundamentally novel deep learning approaches such as on adaptive neural network structures. In all this research, we utilize as well as develop deep learning solutions -- see my publication list at: https://scholar.google.com/citations?user=Ue7-bkkAAAAJ Currently we rely on GPUs on our local computers, where we are running into several bottlenecks with resource allocation and sharing. Access to the national computational resources will help substantially simplify our research efforts and advance their outcome. The requested resource allocation will be used by different project members (with individual NAISS accounts), where the PI will manage their SUPR memberships to the project and overall management of resource demand for fair usage across the project members. Some of the research questions to be studied in this project include deep learning in adaptive radiation therapy; learning techniques in inverse problems of image reconstruction; and adaptive neural networks for automatic network connectivity optimization.