The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

In semi-supervised medical image segmentation, there exist empirical mismatch problems
between labeled and unlabeled data distribution. The knowledge learned from the labeled …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising
performance in many medical image segmentation tasks, they rely on a large set of labeled …

Automatic segmentation of abdominal organs in CT scans plays an important role in clinical
practice. However, most existing benchmarks and datasets only focus on segmentation …

Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …

Deep learning-based semi-supervised learning (SSL) algorithms have led to promising
results in medical images segmentation and can alleviate doctors' expensive annotations by …

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 …

Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …