Abstract Background and Objective Segmenting different tissue components in
histopathological images is of great importance for analyzing tissues and tumor …

Image segmentation is consistently an important task for computer vision and the analysis of
medical images. The analysis and diagnosis of histopathology images by using efficient …

Histopathological image segmentation based on encoder–decoder architectures has
emerged as a pivotal research area in medical image analysis. However, due to the …

Image segmentation remains to be one of the most vital tasks in the area of computer vision
and more so in the case of medical image processing. Image segmentation quality is the …

The Encoder-Decoder architecture is a main stream deep learning model for biomedical
image segmentation. The encoder fully compresses the input and generates encoded …

Background The digital pathology images obtain the essential information about the
patient's disease, and the automated nuclei segmentation results can help doctors make …

Abstract Background and Objective: Segmentation is a key step in biomedical image
analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly …

We propose a novel deep learning method called Boundary-Enhanced Segmentation
Network (BESNet) for the detection and semantic segmentation of cells on histopathological …

We propose a novel multi-level dilated residual neural network, an extension of the classical
U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep …

Convolutional neural networks (CNNs) have strong ability to extract local features, but it is
slightly lacking in extracting global contexts. In contrast, transformers are good at long …