Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

Deep learning has demonstrated remarkable performance across various tasks in medical
imaging. However, these approaches primarily focus on supervised learning, assuming that …

Single-source domain generalization (SDG) in medical image segmentation is a challenging
yet essential task as domain shifts are quite common among clinical image datasets …

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 …

In this paper, we develop a novel transformer-based generative adversarial neural network
called U-Transformer for generalized image outpainting problems. Different from most …

Although unsupervised domain adaptation (UDA) is a promising direction to alleviate
domain shift, they fall short of their supervised counterparts. In this work, we investigate …

Objective: Skin diseases constitute a widespread health concern, and the application of
machine learning and deep learning algorithms has been instrumental in improving …

Medical image segmentation plays an important role in computer-aided diagnosis. Attention
mechanisms that distinguish important parts from irrelevant parts have been widely used in …

Unsupervised domain adaptation (UDA) methods have achieved promising performance in
alleviating the domain shift between different imaging modalities. In this article, we propose …

In order to improve the accuracy of the segmentation of buildings with small sample sizes,
this paper proposes a building-segmentation network, ResFAUnet, with transfer learning …