The U-Net has achieved great success in medical image segmentation. Most U-Nets follow
the encoding–decoding-decision inference path and propagate the features from encoding …

Hybrid architectures based on Convolutional Neural Networks (CNN) and Transformers
have been extensively employed in medical image segmentation. However, previous …

Medical image segmentation is indispensable for diagnosis and prognosis of many
diseases. To improve the segmentation performance, this study proposes a new 2D body …

Abstract Light Sheet Fluorescence Microscopy (LSFM) is increasingly popular in
neuroimaging for its ability to capture high-resolution 3D neural data. However, the …

Medical image segmentation provides a reliable basis for diagnosis analysis and disease
treatment by capturing the global and local features of the target region. To learn global …

Deep learning-based methods have recently shown great promise in medical image
segmentation task. However, CNN-based frameworks struggle with inadequate long-range …

Accurate and comprehensive detection of pavement cracks is important for maintaining road
quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity …

Lung disease analysis in chest X-rays (CXR) using deep learning presents significant
challenges due to the wide variation in lung appearance caused by disease progression …

Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for
preventing the progression of this incurable disease. It is also a challenging task due to …

Medical image segmentation is a critical task for clinical diagnosis and research. However,
dealing with highly imbalanced data remains a significant challenge in this domain, where …