Accurate and automated segmentation of medical images is crucial for enhancing the
efficiency of disease diagnosis and treatment. In the past few years, there has been a …

Deep learning shows excellent performance in medical image segmentation. However, a
pooling operation in its encoding stage leads to feature loss and the ability of multi-scale …

Medical image segmentation and classification are fundamental tasks in computer-aided
diagnosis, where accurate segmentation plays a key role in identifying disease-related …

Objective. Convolutional neural networks have been widely adopted for medical image
segmentation with their outstanding feature representation capabilities. As the segmentation …

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 …

In recent years, encoder-decoder-based network structures have been widely used in
designing medical image segmentation models. However, these methods still face some …

As medical image segmentation has been one of the most widely implemented tasks in
deep learning, there have been various solutions proposed for its applications to achieve …

Medical image segmentation has been widely studied, and many methods have been
proposed. Among the existing methods, U-Net and its variants have achieved a promising …

Segmentation is a fundamental task in medical image analysis. However, most existing
methods focus on primary region extraction and ignore edge information, which is useful for …

Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …