Semi-supervised learning has attracted great attention in the field of machine learning,
especially for medical image segmentation tasks, since it alleviates the heavy burden of …

Deep learning has achieved promising segmentation performance on 3D left atrium MR
images. However, annotations for segmentation tasks are expensive, costly and difficult to …

Training deep convolutional neural networks usually requires a large amount of labeled
data. However, it is expensive and time-consuming to annotate data for medical image …

Accurate segmentation for the left atrium (LA) is a key process of clinical diagnosis and
therapy for atrial fibrillation. In clinical, the semantic-level segmentation of LA consumes …

Accurate, robust and automatic segmentation of the left atrium (LA) in magnetic resonance
images (MRI) is of great significance for studying the LA structure and facilitating the …

Emerging self-ensembling methods have achieved promising semi-supervised
segmentation performances on medical images through forcing consistent predictions of …

Semi-supervised learning provides great significance in left atrium (LA) segmentation model
learning with insufficient labelled data. Generalising semi-supervised learning to cross …

Semi-supervised learning reduces overfitting and facilitates medical image segmentation by
regularizing the learning of limited well-annotated data with the knowledge provided by a …

In medical image segmentation tasks, fully-supervised learning has been a huge success by
using abundant labeled data. However, it is time-consuming and expensive for technicians …

In the recent years, convolutional neural networks have transformed the field of medical
image analysis due to their capacity to learn discriminative image features for a variety of …