Due to the difficulty in accessing a large amount of labeled data, semi-supervised learning is
becoming an attractive solution in medical image segmentation. To make use of unlabeled …

State-of-the-art deep learning models are often trained with a large amount of costly labeled
training data. However, requiring exhaustive manual annotations may degrade the model's …

Semi-supervised learning has greatly advanced medical image segmentation since it
effectively alleviates the need of acquiring abundant annotations from experts, wherein the …

Infrared and visible image fusion has gradually proved to be a vital fork in the field of multi-
modality imaging technologies. In recent developments, researchers not only focus on the …

Semantic segmentation is a fundamental task in computer vision and finds extensive
applications in scene understanding, medical image analysis, and remote sensing. With the …

Abstract Coronavirus disease (COVID-19) has caused a worldwide pandemic, putting
millions of people's health and lives in jeopardy. Detecting infected patients early on chest …

Abstract Background and Objective: Multi-region segmentation by axial lumbar magnetic
resonance imaging (MRI) is important for the diagnosis of lumbar spinal stenosis (LSS) and …

As we all know, semantic segmentation of remote sensing (RS) images is to classify the
images pixel by pixel to realize the semantic decoupling of the images. Most traditional …

Accurate segmentation of tumor regions in automated breast ultrasound (ABUS) images is of
paramount importance in computer-aided diagnosis system. However, the inherent diversity …

Accurate monitoring of raft aquaculture areas (RAAs) is particularly important for the
protection of marine ecosystems. However, existing semantic segmentation methods are …