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 …

Deep learning-based object detection methods using ultrasound images to assist clinicians
in rapid and accurate diagnosis of thyroid nodules has been an attractive area of research …

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 …

Accurate medical image segmentation plays a vital role in clinical practice. Convolutional
Neural Network and Transformer are mainstream architectures for this task. However …

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 …

Glioma segmentation is a crucial task in computer-aided diagnosis, requiring precise
discrimination between lesions and normal tissue at the pixel level. Popular methods …