Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Autosegmentation of gross tumor volumes holds promise to decrease clinical demand and
to provide consistency across clinicians and institutions for radiation treatment planning …

Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …

Deep learning, a new branch of machine learning algorithm, has emerged as a fast growing
trend in medical imaging and become the state-of-the-art method in various clinical …

Abstract Background Phosphorylated Smad3 isoforms are reversible and antagonistic, and
the tumour-suppressive pSmad3C can shift to an oncogenic pSmad3L signal. In addition …

Auto-contouring could revolutionise future planning of radiotherapy treatment. The lack of
consensus on how to assess and validate auto-contouring systems currently limits clinical …

Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that
predominantly impacts the head and neck area. Precise delineation of the Gross Tumor …

The lack of efficient crop phenotypic measurement methods has become a bottleneck in the
field of breeding and precision cultivation. However, high-throughput and accurate …

Purpose Contouring clinical target volume (CTV) from medical images is an essential step
for radiotherapy (RT) planning. Magnetic resonance imaging (MRI) is used as a standard …