8 citations · 9 across the 21 of their papers we have counts for
6 papers · 1 filter
Improving EEG Classification Through Randomly Reassembling Original and Generated Data with Transformer-based Diffusion Models
Mingzhi Chen, Yiyu Gui, Yuqi Su +3
Electroencephalogram (EEG) classification has been widely used in various medical and engineering applications, where it is important for understanding brain function, diagnosing d…
EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classification
Yiyu Gui, MingZhi Chen, Yuqi Su +2
In recent years, with the development of deep learning, electroencephalogram (EEG) classification networks have achieved certain progress. Transformer-based models can perform well…
ProMamba: Prompt-Mamba for polyp segmentation
Jianhao Xie, Ruofan Liao, Ziang Zhang +3
Detecting polyps through colonoscopy is an important task in medical image segmentation, which provides significant assistance and reference value for clinical surgery. However, ac…
MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Model
Guibo Luo, Hanwen Zhang, Xiuling Wang +2
Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framew…
A Segmentation Foundation Model for Diverse-type Tumors
Jianhao Xie, Ziang Zhang, Guibo Luo +1
Large pre-trained models with their numerous model parameters and extensive training datasets have shown excellent performance in various tasks. Many publicly available medical ima…
FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation
Yuxi Liu, Guibo Luo, Yuesheng Zhu
Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything Model (SAM) serves as a powerful foundation model for visual segmentation and can be adapted…