Recent advancements with deep generative models have proven significant potential in the
task of image synthesis, detection, segmentation, and classification. Segmenting the medical …

Multi-contrast magnetic resonance imaging (MRI) is the most common management tool
used to characterize neurological disorders based on brain tissue contrasts. However …

Diffusion MRI (dMRI) is an important neuroimaging technique with high acquisition costs.
Deep learning approaches have been used to enhance dMRI and predict diffusion …

Low-quality (LQ) images often lead to difficulties in the screening and diagnosis of medical
diseases. Unsupervised generative adversarial networks (GAN)-based image enhancement …

Accurate vascular segmentation from High Resolution 3-Dimensional (HR3D) medical
scans is crucial for clinicians to visualize complex vasculature and diagnose related …

Medical image segmentation is a critical field in the domain of computer vision and with the
growing acclaim of deep learning based models, research in this field is constantly …

Loss functions are an essential part in modern data-driven approaches, such as bi-level
training scheme and machine learnings. In this paper, we propose a loss function consisting …

Computed Tomography (CT) is widely used for screening, diagnostic and image-guided
therapy for clinical and research purposes. The current Finite Element Analysis for Research …

Brain tumour comprises a spectrum of malignant and benign entities. The complex
pathophysiology of brain tumours poses challenges to effective clinical decision-making and …