An efficient and effective decoding mechanism is crucial in medical image segmentation
especially in scenarios with limited computational resources. However these decoding …

Rapid advancements in medical image segmentation performance have been significantly
driven by the development of Convolutional Neural Networks (CNNs) and Vision …

Sea–land segmentation is of great significance for autonomous coastline monitoring, which
is fundamental research in the remote sensing community. Due to the diverse contents and …

Segmentation of the temporomandibular joint (TMJ) disc and condyle from magnetic
resonance imaging (MRI) is a crucial task in TMJ internal derangement research. The …

Although the U-shaped architecture, represented by UNet, has become a major network
model for brain tumor segmentation, the repeated convolution and sampling operations can …

The image segmentation of diseases can help clinical diagnosis and treatment in medical
image analysis. Due to the complexity of lesion features (eg, size, location, and morphology) …

In medical images, various types of lesions often manifest significant differences in their
shape and texture. Accurate medical image segmentation demands deep learning models …

In recent years, UNet and its latest extensions, such as UNeXt and TransUNet, have become
the leading medical image segmentation methods. However, medical image usually contain …

Since the emergence of the Corona Virus Disease in 2019 (COVID-19), it has become a
serious health problem affecting the human respiratory system. At present, automatic …

Retinal vessel segmentation is important to assist ophthalmologists in the diagnosis and
treatment of ophthalmic diseases. However, traditional convolutional neural networks …