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publications
PESAM: Privacy-Enhanced Segment Anything Model for Medical Image Segmentation
Published in Advanced Intelligent Computing Technology and Applications (ICIC 2024), 2024
PESAM proposed integrates federated learning with SAM to enable collaborative optimization of medical image segmentation models while preserving data privacy, using a dynamic local aggregation algorithm to enhance performance and generalizability across heterogeneous data.
GM-UNet: GAN-Based VM-UNet Achieves Higher Accuracy
Published in 2024 11th International Conference on Behavioural and Social Computing (BESC), 2024
GM-UNet proposed enhances VM-UNet with a GAN framework and adversarial loss, achieving superior liver CT segmentation performance by better capturing image details.
CFM-UNet: coupling local and global feature extraction networks for medical image segmentation
Published in Scientific Reports, 2025
CFM-UNet proposed integrates CNN-based Bottle2neck blocks for local feature extraction and Mamba-based visual state space blocks for global feature extraction. These parallel frameworks perform feature fusion through our designed SEF block, achieving complementary advantages
Recommended citation: Niu, K., Han, J. & Cai, J. CFM-UNet: coupling local and global feature extraction networks for medical image segmentation. Sci Rep 15, 22236 (2025). https://doi.org/10.1038/s41598-025-92010-y
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MedMaskDiff: Mamba-Based Medical Semantic Image Synthesis for Segmentation
Published in Advanced Intelligent Computing Technology and Applications, 2025
MedMaskDiff proposed is a Mamba-based semantic image synthesis model that generates medical images from masks, which utilizes an evolutionary condition-guide method to enhance the quality and medical logic of the generated target regions.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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