Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

Abstract Fully Convolutional Neural Networks (FCNNs) with contracting and expanding
paths have shown prominence for the majority of medical image segmentation applications …

Change detection (CD) aims to identify surface changes from bitemporal images. In recent
years, deep learning (DL)-based methods have made substantial breakthroughs in the field …

Radiology historically has been a leader of digital transformation in healthcare. The
introduction of digital imaging systems, picture archiving and communication systems …

The state-of-the-art models for medical image segmentation are variants of U-Net and fully
convolutional networks (FCN). Despite their success, these models have two limitations:(1) …

Owing to the success of transformer models, recent works study their applicability in 3D
medical segmentation tasks. Within the transformer models, the self-attention mechanism is …

Deep learning networks have recently been shown to outperform other segmentation
methods on various public, medical-image challenge datasets, particularly on metrics …

Automated prostate segmentation in MRI is highly demanded for computer-assisted
diagnosis. Recently, a variety of deep learning methods have achieved remarkable progress …

Retinal vessel analysis of fundus images is an indispensable method for the screening and
diagnosis of related diseases. In this paper, we propose a novel retinal vessel segmentation …

Purpose Reliable automated segmentation of the prostate is indispensable for image‐
guided prostate interventions. However, the segmentation task is challenging due to …