137 citations · 189 across the 26 of their papers we have counts for
6 papers · 1 filter
Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation
Fenghe Tang, Bingkun Nian, Jianrui Ding +6
In clinical practice, medical image analysis often requires efficient execution on resource-constrained mobile devices. However, existing mobile models-primarily optimized for natu…
U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV
Hongbo Ye, Fenghe Tang, Peiang Zhao +4
Achieving equity in healthcare accessibility requires lightweight yet high-performance solutions for medical image segmentation, particularly in resource-limited settings. Existing…
3DGR-CAR: Coronary artery reconstruction from ultra-sparse 2D X-ray views with a 3D Gaussians representation
Xueming Fu, Yingtai Li, Fenghe Tang +4
Reconstructing 3D coronary arteries is important for coronary artery disease diagnosis, treatment planning and operation navigation. Traditional reconstruction techniques often req…
MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation
Fenghe Tang, Bingkun Nian, Jianrui Ding +4
Due to the scarcity and specific imaging characteristics in medical images, light-weighting Vision Transformers (ViTs) for efficient medical image segmentation is a significant cha…
CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion
Fenghe Tang, Jianrui Ding, Lingtao Wang +2
The U-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a f…
CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network
Fenghe Tang, Lingtao Wang, Chunping Ning +2
U-Net and its extensions have achieved great success in medical image segmentation. However, due to the inherent local characteristics of ordinary convolution operations, U-Net enc…