119 citations · 174 across the 13 of their papers we have counts for
13 papers
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction
Liyan Sun, Shaocong Yu, Chi Zhang +1
Reconstructing MR images using deep neural networks from undersampled k-space data without using fully sampled training references offers significant value in practice, which is a…
A Closer Look at Personalization in Federated Image Classification
Changxing Jing, Yan Huang, Yihong Zhuang +4
Federated Learning (FL) is developed to learn a single global model across the decentralized data, while is susceptible when realizing client-specific personalization in the presen…
AFSC: Adaptive Fourier Space Compression for Anomaly Detection
Haote Xu, Yunlong Zhang, Liyan Sun +3
Anomaly Detection (AD) on medical images enables a model to recognize any type of anomaly pattern without lesion-specific supervised learning. Data augmentation based methods const…
Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation
Chenxin Li, Wenao Ma, Liyan Sun +4
The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a easy task since the fine-detailed connected regions of organ vessel b…
Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding
Liyan Sun, Chenxin Li, Xinghao Ding +3
Despite deep convolutional neural networks achieved impressive progress in medical image computing and analysis, its paradigm of supervised learning demands a large number of annot…
A Teacher-Student Framework for Semi-supervised Medical Image Segmentation From Mixed Supervision
Liyan Sun, Jianxiong Wu, Xinghao Ding +3
Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such learning framework is built on laborious manual ann…
Generator Versus Segmentor: Pseudo-healthy Synthesis
Zhang Yunlong, Li Chenxin, Lin Xin +6
This paper investigates the problem of pseudo-healthy synthesis that is defined as synthesizing a subject-specific pathology-free image from a pathological one. Recent approaches b…
An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection
Liyan Sun, Jiexiang Wang, Yue Huang +3
The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches are mainly based on supervise…
Joint CS-MRI Reconstruction and Segmentation with a Unified Deep Network
Liyan Sun, Zhiwen Fan, Yue Huang +2
The need for fast acquisition and automatic analysis of MRI data is growing in the age of big data. Although compressed sensing magnetic resonance imaging (CS-MRI) has been studied…
A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction
Liyan Sun, Zhiwen Fan, Yue Huang +2
In multi-contrast magnetic resonance imaging (MRI), compressed sensing theory can accelerate imaging by sampling fewer measurements within each contrast. The conventional optimizat…
A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
Zhiwen Fan, Liyan Sun, Xinghao Ding +3
Compressed sensing MRI is a classic inverse problem in the field of computational imaging, accelerating the MR imaging by measuring less k-space data. The deep neural network model…
A Divide-and-Conquer Approach to Compressed Sensing MRI
Liyan Sun, Zhiwen Fan, Xinghao Ding +3
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. In…
A Deep Error Correction Network for Compressed Sensing MRI
Liyan Sun, Zhiwen Fan, Yue Huang +2
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. The goal is to minimize…