activity
20192022
most citedDeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

29 citations · 39 across the 6 of their papers we have counts for

collaborators

6 papers

eess.IV2022★ 1 cited

Invariant Content Synergistic Learning for Domain Generalization of Medical Image Segmentation

Yuxin Kang, Hansheng Li, Xuan Zhao +5

While achieving remarkable success for medical image segmentation, deep convolution neural networks (DCNNs) often fail to maintain their robustness when confronting test data with…

eess.IV2021★ 2 cited

Fast T2w/FLAIR MRI Acquisition by Optimal Sampling of Information Complementary to Pre-acquired T1w MRI

Junwei Yang, Xiao-Xin Li, Feihong Liu +4

Recent studies on T1-assisted MRI reconstruction for under-sampled images of other modalities have demonstrated the potential of further accelerating MRI acquisition of other modal…

eess.IV2021★ 6 cited

Preserving Dense Features for Ki67 Nuclei Detection

Seyed Hossein Mirjahanmardi, Melanie Dawe, Anthony Fyles +4

Nuclei detection is a key task in Ki67 proliferation index estimation in breast cancer images. Deep learning algorithms have shown strong potential in nuclei detection tasks. Howev…

cs.CV2021

Is Perfect Filtering Enough Leading to Perfect Phase Correction for dMRI data?

Liu Feihong, Yang Junwei, He Xiaowei +3

Being complex-valued and low in signal-to-noise ratios, magnitude-based diffusion MRI is confounded by the noise-floor that falsely elevates signal magnitude and incurs bias to the…

cs.CV2019★ 1 cited

Multi-Kernel Filtering for Nonstationary Noise: An Extension of Bilateral Filtering Using Image Context

Feihong Liu, Jun Feng, Pew-Thian Yap +1

Bilateral filtering (BF) is one of the most classical denoising filters, however, the manually initialized filtering kernel hampers its adaptivity across images with various charac…

eess.IV2019★ 29 cited

DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks

Feihong Liu, Jun Feng, Geng Chen +4

Parcellation of whole-brain tractography streamlines is an important step for tract-based analysis of brain white matter microstructure. Existing fiber parcellation approaches rely…