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

Background: Automatic segmentation of medical images has progressed greatly owing to
the development of convolutional neural networks (CNNs). However, there are two …

In this paper, we embed two types of attention modules in the dilated fully convolutional
network (FCN) to solve biomedical image segmentation tasks efficiently and accurately …

Medical image segmentation of tissue abnormalities, key organs, or blood vascular system
is of great significance for any computerized diagnostic system. However, automatic …

Encoder-decoder networks are state-of-the-art approaches to biomedical image
segmentation, but have two problems: ie, the widely used pooling operations may discard …

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 …

Current medical image segmentation approaches have limitations in deeply exploring multi-
scale information and effectively combining local detail textures with global contextual …

Biomedical image segmentation plays a central role in quantitative analysis, clinical
diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) …

The U-Net architecture, built upon the fully convolutional network, has proven to be effective
in biomedical image segmentation. However, U-Net applies skip connections to merge …

Motivated by the recent advances in medical image segmentation using a fully convolutional
network (FCN) called U-Net and its modified variants, we propose a novel improved FCN …