5 citations · 5 across the 4 of their papers we have counts for
4 papers
SPEGC: Continual Test-Time Adaptation via Semantic-Prompt-Enhanced Graph Clustering for Medical Image Segmentation
Xiaogang Du, Jiawei Zhang, Tongfei Liu +2
In medical image segmentation tasks, the domain gap caused by the difference in data collection between training and testing data seriously hinders the deployment of pre-trained mo…
PMR-Net: Parallel Multi-Resolution Encoder-Decoder Network Framework for Medical Image Segmentation
Xiaogang Du, Dongxin Gu, Tao Lei +2
In recent years, encoder-decoder networks have focused on expanding receptive fields and incorporating multi-scale context to capture global features for objects of varying sizes.…
TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation
Rui Sun, Tao Lei, Weichuan Zhang +3
The hybrid architecture of convolution neural networks (CNN) and Transformer has been the most popular method for medical image segmentation. However, the existing networks based o…
Quantum classifiers for domain adaptation
Xi He, Feiyu Du, Mingyuan Xue +3
Transfer learning (TL), a crucial subfield of machine learning, aims to accomplish a task in the target domain with the acquired knowledge of the source domain. Specifically, effec…