[特邀报告]Unsupervised Deep Learning for Fast Imaging: From DAE to Generative - 报告详情

Unsupervised Deep Learning for Fast Imaging: From DAE to Generative
编号:47 访问权限:仅限参会人 更新:2021-11-02 20:09:57 浏览:127次 特邀报告

报告开始:2021年11月14日 14:20 (Asia/Shanghai)

报告时间:25min

所在会议:[PS1] Plenary Session 1 [NM2] Workshop on NM Session 2

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摘要
Reconstruction from very few sampling measurements has recently received a huge boost in performance using supervised deep learning methods. However, while they perform extremely well on data satisfying the conditions they were trained on, their performance deteriorates significantly once these conditions are not satisfied. In this talk, we will introduce some unsupervised deep learning schemes combined with classical iterative procedure for highly under-sampling MRI reconstruction, from denoising autoencoder to score-based generative model. Integrating the learned deep prior knowledge into classical model-based reconstruction, comparable performance can be achieved under various sampling patterns and acceleration factors.
 
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报告人
Qiegen Liu
Professor Nanchang University

Professor, Nanchang University
*Director of the Laboratory for smart sensing and imaging, Nanchang University
*IEEE Senior member
*Member of CCF

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重要日期

摘要提交日期:

2021/08/31

2021/10/25

全文投稿日期:  

2021/09/15

2021/10/25

录取通知日期: 

2021/09/30

2021/11/01

会议日期:   2021-11-12-2021-11-14

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