no code implementations • 5 May 2022 • Yuxin Kang, Hansheng Li, Xuan Zhao, Dongqing Hu, Feihong Liu, Lei Cui, Jun Feng, Lin Yang
In this paper, we propose a method, named Invariant Content Synergistic Learning (ICSL), to improve the generalization ability of DCNNs on unseen datasets by controlling the inductive bias.
no code implementations • 11 Nov 2021 • Junwei Yang, Xiao-Xin Li, Feihong Liu, Dong Nie, Pietro Lio, Haikun Qi, Dinggang Shen
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 modalities.
no code implementations • MICCAI Workshop COMPAY 2021 • Mengkang Lu, Yongsheng Pan, Dong Nie, Feng Shi, Feihong Liu, Yong Xia, Dinggang Shen
In this paper, we propose a Sparse-attention based Multiple Instance contrastive LEarning (SMILE) method for glioma sub-type classification.
no code implementations • 17 Aug 2019 • Feihong Liu, Jun Feng, Pew-Thian Yap, Dinggang Shen
Next, a leaf cluster is used to generate one of the multiple kernels, and two corresponding predecessor clusters are used to fine-tune the adopted kernel.
no code implementations • 7 Jun 2019 • Feihong Liu, Jun Feng, Geng Chen, Ye Wu, Yoonmi Hong, Pew-Thian Yap, Dinggang Shen
GCNNs are capable of extracting the geometric features of each fiber tract and harnessing the resulting features for accurate fiber parcellation and ultimately avoiding the use of atlases and any registration method.