Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …

There is a wide application of deep learning technique to unimodal medical image analysis
with significant classification accuracy performance observed. However, real-world …

Medical imaging methods using multiple modalities are used more frequently in both clinical
settings and academic studies. The use of ensemble learning and associated multimodal …

Deep learning on a limited number of labels/annotations is a challenging task for medical
imaging analysis. In this paper, we propose a novel self-training segmentation pipeline (Self …

Accurate liver segment segmentation based on radiological images is indispensable for the
preoperative analysis of liver tumor resection surgery. However, most of the existing …

Development of deep learning systems for biomedical segmentation often requires access
to expert-driven, manually annotated datasets. If more than a single expert is involved in the …

Clinical MR imaging is typically qualitative, ie the observed signal reflects the underlying
tissue contrast, but the measurements are not meaningful when taken in isolation …

Classifying 3D MRI images for early detection of Alzheimer's disease is a critical task in
medical imaging. Traditional approaches using Convolutional Neural Networks (CNNs) and …

Clinical MR imaging is typically qualitative, ie the observed signal reflects the underlying
tissue contrast, but the measurements are not meaningful when taken in isolation …

Magnetic Resonance Imaging (MRI) is a powerful, non-invasive medical imaging modality
adept at showcasing soft-tissue contrast and well-suited to imaging most body parts …