In the last decade, deep neural networks have been widely applied to medical image
segmentation, achieving good results in computer-aided diagnosis tasks etc. However, the …

Convolutional neural networks (CNNs) have been the consensus for medical image
segmentation tasks. However, they inevitably suffer from the limitation in modeling long …

The segmentation of tumours requires accurate identification and localisation in medical
images. With the advent of U-Net and its variants, medical image segmentation has …

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

Abstract Convolutional Neural Network-based UNet and its variants have shown remarkable
performance in medical image segmentation. However, these methods can only capture …

In recent years, convolutional neural networks (CNNs) have made great achievements in the
field of medical image segmentation, especially full convolutional neural networks based on …

The convolutional neural network-based methods have become more and more popular for
medical image segmentation due to their outstanding performance. However, they struggle …

Purpose Convolution operator-based neural networks have shown great success in medical
image segmentation over the past decade. The U-shaped network with a codec structure is …

Background and objective Convolutional neural networks (CNNs) play an important role in
the field of medical image segmentation. Among many kinds of CNNs, the U-net architecture …

Transformers have shown great promise in medical image segmentation due to their ability
to capture long-range dependencies through self-attention. However, they lack the ability to …