The advancement of medical imaging has profoundly impacted our understanding of the
human body and various diseases. It has led to the continuous refinement of related …

The powerful data processing and pattern recognition capabilities of machine learning (ML)
technology have provided technical support for the innovation in computational chemistry …

Medical image segmentation is increasingly reliant on deep learning techniques, yet the
promising performance often come with high annotation costs. This paper introduces Weak …

Background The issue of using deep learning to obtain accurate gross tumor volume (GTV)
and metastatic lymph nodes (MLN) segmentation for nasopharyngeal carcinoma (NPC) on …

Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery
has become a crucial technique in land surveys. Cropland is highly heterogeneous and …

Achieving accurate bladder wall and tumor segmentation from MRI is critical for diagnosing
and treating bladder cancer. However, automated segmentation remains challenging due to …

Background: As an important part of the tongue, the tongue coating is closely associated
with different disorders and has major diagnostic benefits. This study aims to construct a …

Medical image segmentation serves as an important tool in the treatment of various medical
diseases. However, achieving precise and efficient segmentation remains challenging due …

Network architectures deriving from the Unet framework and its convolutional neural network
variants have garnered significant attention for their impressive feats in computer vision …

Hybrid architectures based on Convolutional Neural Network (CNN) and Vision Transformer
(ViT) have become an important research direction in medical image segmentation in recent …