Abstract Background and Objectives: Transformer is a model relying entirely on self-
attention which has a wide range of applications in the field of natural language processing …

In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Abstract 3D MRI Brain Tumor Segmentation is of great significance in clinical diagnosis and
treatment. Accurate segmentation results are critical for localization and spatial distribution …

Medical image segmentation faces current challenges in effectively extracting and fusing
long-distance and local semantic information, as well as mitigating or eliminating semantic …

Compared with cast-in-place reinforced concrete structures, precast concrete (PC) structures
have a higher possibility of concentrated plasticity development in connections while …

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …

Accurate segmentation of nasopharyngeal carcinoma (NPC) in magnetic resonance (MR)
images is crucial for radiotherapy planning. However, vanilla 2D/3D deep convolutional …

We propose a shape prior representation-constrained multi-scale features fusion
segmentation network for medical image segmentation, including training and testing …

Improving overall performance is the ultimate goal of any machine learning (ML) algorithm.
While it is a trivial task to explore multiple individual validation measurements, evaluating …

Magnetic resonance imaging (MRI) is critically important for brain mapping in both scientific
research and clinical studies. Precise segmentation of brain tumors facilitates clinical …