3 citations · 3 across the 1 of their papers we have counts for
8 papers
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…
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…