1 citations · 1 across the 5 of their papers we have counts for
6 papers
Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation
Hong Zheng, Nan Mu, Han Su +2
Noise reduction constitutes a crucial operation within Digital Signal Processing. Regrettably, it frequently remains neglected when dealing with the processing of convolutional fea…
SFD-Mamba2Net: Structure-Guided Frequency-Enhanced Dual-Stream Mamba2 Network for Coronary Artery Segmentation
Nan Mu, Ruiqi Song, Zhihui Xu +2
Background: Coronary Artery Disease (CAD) is one of the leading causes of death worldwide. Invasive Coronary Angiography (ICA), regarded as the gold standard for CAD diagnosis, nec…
An Uncertainty-Aware Dynamic Decision Framework for Progressive Multi-Omics Integration in Classification Tasks
Nan Mu, Hongbo Yang, Chen Zhao
Background and Objective: High-throughput multi-omics technologies have proven invaluable for elucidating disease mechanisms and enabling early diagnosis. However, the high cost of…
FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li +3
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (IC…
Multi-Disease-Aware Training Strategy for Cardiac MR Image Segmentation
Hong Zheng, Yucheng Chen, Nan Mu +1
Accurate segmentation of the ventricles from cardiac magnetic resonance images (CMRIs) is crucial for enhancing the diagnosis and analysis of heart conditions. Deep learning-based…
Parameter-Efficient Transformer with Hybrid Axial-Attention for Medical Image Segmentation
Yiyue Hu, Lei Zhang, Nan Mu +1
Transformers have achieved remarkable success in medical image analysis owing to their powerful capability to use flexible self-attention mechanism. However, due to lacking intrins…