Although various methods based on convolutional neural networks have improved the
performance of biomedical image segmentation to meet the precision requirements of …

Medical image segmentation is important for computer-aided diagnosis. Good segmentation
demands the model to see the big picture and fine details simultaneously, ie, to learn image …

Purpose Localization and screening of the target tissue is a main prerequisite of numerous
medical procedures, including capsule endoscopy, colonoscopy and histology …

Medical image segmentation is crucial for medical image processing and the development
of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks …

Abstract Background and Objective Medical image segmentation has garnered significant
research attention in the neural network community as a fundamental requirement for …

Medical image segmentation is a crucial task in the field of medical image analysis.
Harmonizing the convolution and multi-head self-attention mechanism is a recent research …

U-Net has become a standard model for medical image segmentation, alleviating the
challenges posed by the costly acquisition and labeling of medical data. The convolutional …

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …

Precise segmentation is vital for successful diagnosis and treatment planning. Medical
image segmentation has demonstrated remarkable advances with the introduction of deep …

Manually inspecting polyps from a colonoscopy for colorectal cancer or performing a biopsy
on skin lesions for skin cancer are time-consuming, laborious, and complex procedures …