no code implementations • 22 Apr 2022 • Changxing Jing, Yan Huang, Yihong Zhuang, Liyan Sun, Yue Huang, Zhenlong Xiao, Xinghao Ding
This paper shows that it is possible to achieve flexible personalization after the convergence of the global model by introducing representation learning.
no code implementations • 17 Apr 2022 • Haote Xu, Yunlong Zhang, Liyan Sun, Chenxin Li, Yue Huang, Xinghao Ding
Data augmentation based methods construct pseudo-healthy images by "pasting" fake lesions on real healthy ones, and a network is trained to predict healthy images in a supervised manner.
no code implementations • 31 May 2021 • Chenxin Li, Wenao Ma, Liyan Sun, Xinghao Ding, Yue Huang, Guisheng Wang, Yizhou Yu
In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels.
no code implementations • 10 Dec 2020 • Liyan Sun, Chenxin Li, Xinghao Ding, Yue Huang, Guisheng Wang, Yizhou Yu
Motivated by the spatial consistency and regularity in medical images, we developed an efficient global correlation module to capture the correlation between a support and query image and incorporate it into the deep network called global correlation network.
1 code implementation • 23 Oct 2020 • Liyan Sun, Jianxiong Wu, Xinghao Ding, Yue Huang, Guisheng Wang, Yizhou Yu
We further proposed a localization branch realized via an aggregation of high-level features in a deep decoder to predict locations of organ and lesion, which enriches student segmentor with precise localization information.
no code implementations • 25 Oct 2018 • Liyan Sun, Jiexiang Wang, Yue Huang, Xinghao Ding, Hayit Greenspan, John Paisley
Being able to provide a "normal" counterpart to a medical image can provide useful side information for medical imaging tasks like lesion segmentation or classification validated by our experiments.
no code implementations • 6 May 2018 • Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley
The need for fast acquisition and automatic analysis of MRI data is growing in the age of big data.
no code implementations • 10 Apr 2018 • Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley
In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast.
no code implementations • ECCV 2018 • Zhiwen Fan, Liyan Sun, Xinghao Ding, Yue Huang, Congbo Cai, John Paisley
In this paper, we proposed a segmentation-aware deep fusion network called SADFN for compressed sensing MRI.
no code implementations • 27 Mar 2018 • Liyan Sun, Zhiwen Fan, Xinghao Ding, Congbo Cai, Yue Huang, John Paisley
Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires.
no code implementations • 23 Mar 2018 • Liyan Sun, Zhiwen Fan, Yue Huang, Xinghao Ding, John Paisley
Existing CS-MRI algorithms can serve as the template module for guiding the reconstruction.