Medical image segmentation is an important task in modern analysis of medical images.
Current methods tend to extract either local features with convolutions or global features with …

In recent years, hybrid modeling approaches that integrate U-shaped networks with
Transformer architectures have demonstrated significant potential in achieving precise and …

For the task of medical image segmentation, fully convolutional network (FCN) based
architectures have been extensively used with various modifications. A rising trend in these …

Accurate and automated segmentation of medical images plays a crucial role in diagnostic
evaluation and treatment planning. In recent years, hybrid models have gained considerable …

Methods based on convolutional neural networks have improved the performance of
biomedical image segmentation. However, most of these methods cannot efficiently …

Medical image segmentation is an important and complex task in clinical practices, but the
widely used U-Net usually cannot achieve satisfactory performances in some clinical …

A variety of convolutional neural network (CNN) based methods for medical image
segmentation have achieved outstanding performance, however, inherently suffered from a …

In this paper, we propose a multipath feature fusion convolutional neural network (MF2-Net)
with novel and efficient spatial group convolution (SGC) modules with a multipath feature …

Medical image segmentation result is an essential reference for disease diagnosis.
Recently, with the development and application of convolutional neural networks, medical …

Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis
and monitoring. However, due to the complexity of medical images and the limitations of …