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eess.IV2025★ 4 cited
MedVKAN: Efficient Feature Extraction with Mamba and KAN for Medical Image Segmentation
Hancan Zhu, Jinhao Chen, Guanghua He
Medical image segmentation has traditionally relied on convolutional neural networks (CNNs) and Transformer-based models. CNNs, however, are constrained by limited receptive fields…
eess.IV2025
tCURLoRA: Tensor CUR Decomposition Based Low-Rank Parameter Adaptation and Its Application in Medical Image Segmentation
Guanghua He, Wangang Cheng, Hancan Zhu +2
Transfer learning, by leveraging knowledge from pre-trained models, has significantly enhanced the performance of target tasks. However, as deep neural networks scale up, full fine…
eess.IV2024
A Novel Hybrid Parameter-Efficient Fine-Tuning Approach for Hippocampus Segmentation and Alzheimer's Disease Diagnosis
Wangang Cheng, Guanghua He, Keli Hu +4
Deep learning methods have significantly advanced medical image segmentation, yet their success hinges on large volumes of manually annotated data, which require specialized expert…