Medical image semantic segmentation is a crucial technique in medical imaging processing,
providing essential diagnostic support by precisely delineating different tissue structures and …

A pyramidical multi-scale encoder-decoder network, namely PMED-Net, is proposed for
medical image segmentation. Different variants of encoder-decoder networks are in practice …

Medical image semantic segmentation plays a crucial role in the localization of organs and
lesions, analysis and quantification of pathologies, and surgical planning and navigation …

Abstract Background and Objective: Segmentation is a key step in biomedical image
analysis tasks. Recently, convolutional neural networks (CNNs) have been increasingly …

Considering the complexity of lesion regions in medical images, current researches relying
on CNNs typically employ large-kernel convolutions to expand the receptive field and …

Segmentation is an important task in a wide range of computer vision applications, including
medical image analysis. Recent years have seen an increase in the complexity of medical …

The automatic medical image segmentation technology based on deep learning has
achieved high accuracy, but there is a dilemma that the parameters are huge and difficult to …

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

Semantic segmentation of 3D medical images plays an important role in assisting
physicians in diagnosing and successively studying the progression of the disease. In recent …

Medical image segmentation plays a vital role in various clinical applications, enabling
accurate delineation and analysis of anatomical structures or pathological regions …