Background The increased availability and usage of modern medical imaging induced a
strong need for automatic medical image segmentation. Still, current image segmentation …

Recently, ConvNeXts constructing from standard ConvNet modules has produced
competitive performance in various image applications. In this paper, an efficient model …

Residual network (ResNet) and densely connected network (DenseNet) have significantly
improved the training efficiency and performance of deep convolutional neural networks …

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 …

Detection of colon polyps has become a trending topic in the intersecting fields of machine
learning and gastrointestinal endoscopy. The focus has mainly been on per-frame …

We propose a novel technique to incorporate attention within convolutional neural networks
using feature maps generated by a separate convolutional autoencoder. Our attention …

Fueled by the diversity of datasets, semantic segmentation is a popular subfield in medical
image analysis with a vast number of new methods being proposed each year. This ever …

Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …

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 …

The advanced development of deep learning methods has recently made significant
improvements in medical image segmentation. Encoder–decoder networks, such as U-Net …