Search Results for author: Zhenzhou Wu

Found 8 papers, 1 papers with code

Network resilience in the aging brain

no code implementations3 Feb 2022 Tao Liu, Shu Guo, Hao liu, Rui Kang, Mingyang Bai, Jiyang Jiang, Wei Wen, Xing Pan, Jun Tai, JianXin Li, Jian Cheng, Jing Jing, Zhenzhou Wu, Haijun Niu, Haogang Zhu, Zixiao Li, Yongjun Wang, Henry Brodaty, Perminder Sachdev, Daqing Li

Degeneration and adaptation are two competing sides of the same coin called resilience in the progressive processes of brain aging or diseases.

Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss

1 code implementation6 Jun 2021 Jian Cheng, Ziyang Liu, Hao Guan, Zhenzhou Wu, Haogang Zhu, Jiyang Jiang, Wei Wen, DaCheng Tao, Tao Liu

In this paper, a novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data.

Age Estimation

AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks

no code implementations5 Oct 2018 Evelyn Chee, Zhenzhou Wu

Our proposed method was evaluated on magnetic resonance images of the axial view of human brain and compared with the performance of a conventional image registration method.

Affine Image Registration Image Registration +1

HiNet: Hierarchical Classification with Neural Network

no code implementations31 May 2017 Zhenzhou Wu, Sean Saito

Traditionally, classifying large hierarchical labels with more than 10000 distinct traces can only be achieved with flatten labels.

Classification General Classification

Character-Based Text Classification using Top Down Semantic Model for Sentence Representation

no code implementations29 May 2017 Zhenzhou Wu, Xin Zheng, Daniel Dahlmeier

Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets.

General Classification text-classification +1

Multi-Modal Hybrid Deep Neural Network for Speech Enhancement

no code implementations15 Jun 2016 Zhenzhou Wu, Sunil Sivadas, Yong Kiam Tan, Ma Bin, Rick Siow Mong Goh

Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal.

Speech Enhancement

Deep Denoising Auto-encoder for Statistical Speech Synthesis

no code implementations17 Jun 2015 Zhenzhou Wu, Shinji Takaki, Junichi Yamagishi

This paper proposes a deep denoising auto-encoder technique to extract better acoustic features for speech synthesis.

Denoising Speech Synthesis

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