Semi-supervised learning (SSL) has recently demonstrated great success in medical image
segmentation, significantly enhancing data efficiency with limited annotations. However …

Video object segmentation (VOS) plays an important role in video analysis and
understanding, which in turn facilitates a number of diverse applications, including video …

Objective. Deep learning networks such as convolutional neural networks (CNN) and
Transformer have shown excellent performance on the task of medical image segmentation …

Polyp segmentation technology based on deep learning can quickly and accurately help
doctors locate lesions, but its development is limited by pixel-level annotations. The polyp …

In recent years, artificial intelligence has been applied to 3D COVID-19 medical image
diagnosis, which reduces detection costs and missed diagnosis rates with higher predictive …

In medical images, the edges of organs are often blurred and unclear. Existing semi-
supervised image segmentation methods rarely model edges explicitly. Thus most methods …

Deep learning models have demonstrated significant effectiveness in addressing intricate
object segmentation and image classification tasks. Nevertheless, their widespread use is …

Semi-supervised learning (SSL) is a powerful tool to address the challenge of insufficient
annotated data in medical segmentation problems. However, existing semi-supervised …

Semi-supervised semantic segmentation based on deep learning is crucial for ultrasound
image analysis. However, the scattering noise of ultrasound images decreases the network …

Co-training is a semi-supervised algorithm that aims to improve prediction effects by
exchanging confident instances and pseudo-labels among multiple learners. One central …