Semantic segmentation is a fundamental but challenging problem of pixel-level remote
sensing (RS) data analysis. Semantic segmentation tasks based on aerial and satellite …

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 …

Semantic image segmentation is a popular image segmentation technique where each pixel
in an image is labeled with an object class. This technique has become a vital part of image …

An efficient and effective decoding mechanism is crucial in medical image segmentation
especially in scenarios with limited computational resources. However these decoding …

In this paper, we are the first to propose a new graph convolution-based decoder namely,
Cascaded Graph Convolutional Attention Decoder (G-CASCADE), for 2D medical image …

Medical image segmentation is an essential task in clinical diagnosis and case analysis.
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …

Abstract Segmentation of abdominal Computed Tomography (CT) scan is essential for
analyzing, diagnosing, and treating visceral organ diseases (eg, hepatocellular carcinoma) …

Transformers have shown great success in medical image segmentation. However,
transformers may exhibit a limited generalization ability due to the underlying single-scale …

Medical image segmentation plays a vital role in computer-aided diagnosis procedures.
Recently, U-Net is widely used in medical image segmentation. Many variants of U-Net have …

Convolution operation is performed within a local window of the input image. Therefore,
convolutional neural network (CNN) is skilled in obtaining local information. Meanwhile, the …