UNet has been highly successful in various medical image segmentation tasks, but the
restricted field of perception of convolutional operations has led to the lack of UNet's ability …

This article presents a simple yet effective multilayer perceptron (MLP) architecture, namely
CycleMLP, which is a versatile neural backbone network capable of solving various tasks of …

In the process of cancer diagnosis and treatment, accurate extraction of the lesion area
helps the doctor to judge the condition. Currently, medical image segmentation algorithms …

Detection and segmentation of cell nuclei in hematoxylin and eosin-stained tissue images
pose critical clinical challenges, including intricate backgrounds, variable nuclear …

In the context of robotic grasping, object segmentation encounters several difficulties when
faced with dynamic conditions such as real-time operation, occlusion, low lighting, motion …

Convolution-based methods are increasingly being used in medical image segmentation
tasks and have shown good performance, but there are always problems in segmenting …

Models based on U-like structures have improved the performance of medical image
segmentation. However, the single-layer decoder structure of U-Net is too" thin" to exploit …

Abstract Previously, Multi-Layer Perceptrons (MLPs) were primarily used in image
classification tasks. The emergence of the MLP-Mixer architecture has demonstrated the …

Traditional U-shape segmentation models can achieve excellent performance with an
elegant structure. However, the single-layer decoder structure of U-Net or SwinUnet is too …

Background: Intervertebral disc degeneration (IVD) is now the most prevalent disease in the
world; thus, precise intervertebral disc segmentation is essential for the assessment and …