Medical image segmentation's accuracy is crucial for clinical analysis and diagnosis.
Despite progress with U-Net-inspired models, they often underuse multi-scale encoding …

Medical image segmentation's accuracy is crucial for clinical analysis and diagnosis.
Despite progress with U-Net-inspired models, they often underuse multi-scale convolutional …

The advanced development of deep learning methods has recently made significant
improvements in medical image segmentation. Encoder–decoder networks, such as U-Net …

Encoder-decoder architectures have been extensively used for semantic segmentation
tasks, including biomedical image segmentation. Most architectures utilize UNet as the base …

Automatic segmentation of medical images is a difficult task in the field of computer vision
owing to the various backgrounds, shapes, size, and colors of polyps or tumors. Despite the …

In recent years, various deep-learning methodologies have been developed for processing
medical images, with Unet and its derivatives proving particularly effective in medical image …

Current state-of-the-art medical image segmentation techniques predominantly employ the
encoder–decoder architecture. Despite its widespread use, this U-shaped framework …

In recent years, U‐Net and its variants have gained widespread use in medical image
segmentation. One key aspect of U‐Net's design is the skip connection, facilitating the …

Accurate segmentation is crucial in medical image diagnosis and treatment planning.
Currently, segmentation methods in medical images primarily utilize Transformer and …

Medical image segmentation techniques based on convolution neural networks indulge in
feature extraction triggering redundancy of parameters and unsatisfactory target localization …