TAC-UNet: transformer-assisted convolutional neural network for medical image segmentation.

ABSTRACT:

Background

Medical image segmentation is crucial for improving healthcare outcomes. Convolutional neural networks (CNNs) have been widely applied in medical image analysis; however, their inherent inductive biases limit their ability to capture global contextual information. Vision transformer (ViT) architectures address this limitation by leveraging attention mechanisms to model global relationships; however, they typically require large-scale datasets for effective training, which is challenging in the field of medical imaging due to limited data availability. This study aimed to integrate the advantages of CNN and ViT architectures to improve segmentation performance on small-scale medical image datasets.

Methods

In this study, we established a U-shaped network architectu

SUBMITTER: He J 

PROVIDER: S-EPMC11651933 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature