This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …

Magnetic miniature robots are promising tools for minimally invasive and noninvasive
therapy. Constructing systems with actuation–perception loops is an essential step to …

Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …

The automatic segmentation of brain tumors in Magnetic Resonance Imaging (MRI) plays a
major role in accurate diagnosis and treatment planning. The present study proposes a new …

Access to sufficient annotated data is a common challenge in training deep neural networks
on medical images. As annotating data is expensive and time-consuming, it is difficult for an …

Medical image segmentation is a typical task in medical image processing and critical
foundation in medical image analysis. U-Net is well-liked in medical image segmentation …

Deep neural networks enable highly accurate image segmentation, but require large
amounts of manually annotated data for supervised training. Few-shot learning aims to …

It is critical to quantitatively analyse the developing human fetal brain in order to fully
understand neurodevelopment in both normal fetuses and those with congenital disorders …

Compared with the rapidly growing artificial intelligence (AI) research in other branches of
healthcare, the pace of developing AI capacities in dental care is relatively slow. Dental care …

Whole brain segmentation of fine-grained structures using deep learning (DL) is a very
challenging task since the number of anatomical labels is very high compared to the number …