Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

no code implementations • 16 Jun 2023 • Dicheng Chen, Meijin Lin, Huiting Liu, Jiayu Li, Yirong Zhou, Taishan Kang, Liangjie Lin, Zhigang Wu, Jiazheng Wang, Jing Li, Jianzhong Lin, Xi Chen, Di Guo, Xiaobo Qu

Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.

Cloud Computing

WikiLink: an encyclopedia-based semantic network for design innovation

1 code implementation • 30 Aug 2022 • Haoyu Zuo, Qianzhi Jing, Tianqi Song, Huiting Liu, Lingyun Sun, Peter Childs, Liuqing Chen

However, existing semantic networks for design innovation is built on data source restricted to technological and scientific information.

Model Stability with Continuous Data Updates

no code implementations • 14 Jan 2022 • Huiting Liu, Avinesh P. V. S., Siddharth Patwardhan, Peter Grasch, Sachin Agarwal

For this study, we propose a methodology for the assessment of model stability (which we refer to as jitter under various experimental conditions.

text-classification Text Classification

Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data

no code implementations • 26 Jan 2021 • Dicheng Chen, Wanqi Hu, Huiting Liu, Yirong Zhou, Tianyu Qiu, Yihui Huang, Zi Wang, Jiazheng Wang, Liangjie Lin, Zhigang Wu, Hao Chen, Xi Chen, Gen Yan, Di Guo, Jianzhong Lin, Xiaobo Qu

A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).

Denoising

RubyStar: A Non-Task-Oriented Mixture Model Dialog System

no code implementations • 8 Nov 2017 • Huiting Liu, Tao Lin, Hanfei Sun, Weijian Lin, Chih-Wei Chang, Teng Zhong, Alexander Rudnicky

RubyStar is a dialog system designed to create "human-like" conversation by combining different response generation strategies.

Question Answering Response Generation +1