Search Results for author: Yike Guo

Found 72 papers, 22 papers with code

Improving Deep Embedded Clustering via Learning Cluster-level Representations

no code implementations COLING 2022 Qing Yin, Zhihua Wang, Yunya Song, Yida Xu, Shuai Niu, Liang Bai, Yike Guo, Xian Yang

In this paper, we propose a novel DEC model, which we named the deep embedded clustering model with cluster-level representation learning (DECCRL) to jointly learn cluster and instance level representations.

Clustering Contrastive Learning +2

Continual Learning with Dirichlet Generative-based Rehearsal

no code implementations13 Sep 2023 Min Zeng, Wei Xue, Qifeng Liu, Yike Guo

Recent advancements in data-driven task-oriented dialogue systems (ToDs) struggle with incremental learning due to computational constraints and time-consuming issues.

Continual Learning Incremental Learning +6

Video-Instrument Synergistic Network for Referring Video Instrument Segmentation in Robotic Surgery

no code implementations18 Aug 2023 Hongqiu Wang, Lei Zhu, Guang Yang, Yike Guo, Shichen Zhang, Bo Xu, Yueming Jin

Our method is verified on these datasets, and experimental results exhibit that the VIS-Net can significantly outperform existing state-of-the-art referring segmentation methods.

Robot Navigation

A generative model for surrogates of spatial-temporal wildfire nowcasting

no code implementations5 Aug 2023 Sibo Cheng, Yike Guo, Rossella Arcucci

The model is tested in the ecoregion of a recent massive wildfire event in California, known as the Chimney fire.

Temporal Sequences

On the Effectiveness of Speech Self-supervised Learning for Music

no code implementations11 Jul 2023 Yinghao Ma, Ruibin Yuan, Yizhi Li, Ge Zhang, Xingran Chen, Hanzhi Yin, Chenghua Lin, Emmanouil Benetos, Anton Ragni, Norbert Gyenge, Ruibo Liu, Gus Xia, Roger Dannenberg, Yike Guo, Jie Fu

Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech.

Information Retrieval Music Information Retrieval +2

LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT

no code implementations29 Jun 2023 Le Zhuo, Ruibin Yuan, Jiahao Pan, Yinghao Ma, Yizhi Li, Ge Zhang, Si Liu, Roger Dannenberg, Jie Fu, Chenghua Lin, Emmanouil Benetos, Wenhu Chen, Wei Xue, Yike Guo

We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal.

Language Modelling Large Language Model +2

MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

1 code implementation31 May 2023 Yizhi Li, Ruibin Yuan, Ge Zhang, Yinghao Ma, Xingran Chen, Hanzhi Yin, Chenghua Lin, Anton Ragni, Emmanouil Benetos, Norbert Gyenge, Roger Dannenberg, Ruibo Liu, Wenhu Chen, Gus Xia, Yemin Shi, Wenhao Huang, Yike Guo, Jie Fu

To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training.

Language Modelling Quantization +1

Interactive Natural Language Processing

no code implementations22 May 2023 Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao, Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, Qingqing Zhu, Zhenzhu Yang, Adam Nik, Qi Liu, Chenghua Lin, Shi Wang, Ruibo Liu, Wenhu Chen, Ke Xu, Dayiheng Liu, Yike Guo, Jie Fu

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP, aimed at addressing limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence.

Decision Making

NAS-FM: Neural Architecture Search for Tunable and Interpretable Sound Synthesis based on Frequency Modulation

no code implementations22 May 2023 Zhen Ye, Wei Xue, Xu Tan, Qifeng Liu, Yike Guo

Since expert knowledge is hard to acquire, it hinders the flexibility to quickly design and tune digital synthesizers for diverse sounds.

Neural Architecture Search

Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects

no code implementations16 May 2023 Chenhong Zhou, Xiaorui Zhang, Meng Gao, Shanshan Liu, Yike Guo, Jie Chen

Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants.


CoMoSpeech: One-Step Speech and Singing Voice Synthesis via Consistency Model

1 code implementation11 May 2023 Zhen Ye, Wei Xue, Xu Tan, Jie Chen, Qifeng Liu, Yike Guo

In this paper, we propose a "Co"nsistency "Mo"del-based "Speech" synthesis method, CoMoSpeech, which achieve speech synthesis through a single diffusion sampling step while achieving high audio quality.

Denoising Singing Voice Synthesis +1

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics

no code implementations8 Mar 2023 Fernando E. Rosas, Diego Candia-Rivera, Andrea I Luppi, Yike Guo, Pedro A. M. Mediano

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance.

Bayesian Inference Decision Making

Pathway to Future Symbiotic Creativity

no code implementations18 Aug 2022 Yike Guo, Qifeng Liu, Jie Chen, Wei Xue, Jie Fu, Henrik Jensen, Fernando Rosas, Jeffrey Shaw, Xing Wu, Jiji Zhang, Jianliang Xu

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation.


A Dual-Masked Auto-Encoder for Robust Motion Capture with Spatial-Temporal Skeletal Token Completion

1 code implementation15 Jul 2022 Junkun Jiang, Jie Chen, Yike Guo

In order to demonstrate the proposed model's capability in dealing with severe data loss scenarios, we contribute a high-accuracy and challenging motion capture dataset of multi-person interactions with severe occlusion.

Suggestive Annotation of Brain MR Images with Gradient-guided Sampling

no code implementations2 Jun 2022 Chengliang Dai, Shuo Wang, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai

We evaluate the framework on two different brain image analysis tasks, namely brain tumour segmentation and whole brain segmentation.

Brain Segmentation Image Segmentation +1

Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint

no code implementations24 May 2022 Heng-Yi Wu, Jingqing Zhang, Julia Ive, Tong Li, Vibhor Gupta, Bingyuan Chen, Yike Guo

Structured (tabular) data in the preclinical and clinical domains contains valuable information about individuals and an efficient table-to-text summarization system can drastically reduce manual efforts to condense this data into reports.

Data Augmentation Table-to-Text Generation +1

A Scalable Workflow to Build Machine Learning Classifiers with Clinician-in-the-Loop to Identify Patients in Specific Diseases

no code implementations18 May 2022 Jingqing Zhang, Atri Sharma, Luis Bolanos, Tong Li, Ashwani Tanwar, Vibhor Gupta, Yike Guo

This paper proposes a scalable workflow which leverages both structured data and unstructured textual notes from EHRs with techniques including NLP, AutoML and Clinician-in-the-Loop mechanism to build machine learning classifiers to identify patients at scale with given diseases, especially those who might currently be miscoded or missed by ICD codes.

AutoML Specificity

Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge

no code implementations19 Apr 2022 Ashwani Tanwar, Jingqing Zhang, Julia Ive, Vibhor Gupta, Yike Guo

Extracting phenotypes from clinical text has been shown to be useful for a variety of clinical use cases such as identifying patients with rare diseases.

Word Embeddings

Receding Neuron Importances for Structured Pruning

no code implementations13 Apr 2022 Mihai Suteu, Yike Guo

To tackle this issue, we introduce a simple BatchNorm variation with bounded scaling parameters, based on which we design a novel regularisation term that suppresses only neurons with low importance.

Label-dependent and event-guided interpretable disease risk prediction using EHRs

1 code implementation18 Jan 2022 Shuai Niu, Yunya Song, Qing Yin, Yike Guo, Xian Yang

Thirdly, both label-dependent and event-guided representations are integrated to make a robust prediction, in which the interpretability is enabled by the attention weights over words from medical notes.

Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records

1 code implementation18 Jan 2022 Shuai Niu, Qing Yin, Yunya Song, Yike Guo, Xian Yang

In this paper, we propose a label dependent attention model LDAM to 1) improve the interpretability by exploiting Clinical-BERT (a biomedical language model pre-trained on a large clinical corpus) to encode biomedically meaningful features and labels jointly; 2) extend the idea of joint embedding to the processing of time-series data, and develop a multi-modal learning framework for integrating heterogeneous information from medical notes and time-series health status indicators.

Language Modelling Time Series +1

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

1 code implementation19 Dec 2021 Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Datwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gomez, Pablo Arbelaez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-min Pei, Murat AK, Sarahi Rosas-Gonzalez, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Lofstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh McHugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-Andre Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation.

Benchmarking Brain Tumor Segmentation +3

OmiTrans: generative adversarial networks based omics-to-omics translation framework

1 code implementation27 Nov 2021 XiaoYu Zhang, Yike Guo

With the rapid development of high-throughput experimental technologies, different types of omics (e. g., genomics, epigenomics, transcriptomics, proteomics, and metabolomics) data can be produced from clinical samples.

Image-to-Image Translation Translation

Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases

no code implementations24 Jul 2021 Jingqing Zhang, Luis Bolanos, Ashwani Tanwar, Julia Ive, Vibhor Gupta, Yike Guo

We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU).


Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

no code implementations8 Jul 2021 Shuo Wang, Chen Qin, Nicolo Savioli, Chen Chen, Declan O'Regan, Stuart Cook, Yike Guo, Daniel Rueckert, Wenjia Bai

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.

Anatomy Cardiac Segmentation +1

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

2 code implementations26 May 2021 Eloise Withnell, XiaoYu Zhang, Kai Sun, Yike Guo

To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE.

Classification Clustering +1

Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations

1 code implementation13 Apr 2021 César Quilodrán-Casas, Rossella Arcucci, Laetitia Mottet, Yike Guo, Christopher Pain

Our two-step method integrates a Principal Components Analysis (PCA) based adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM) networks.

Time Series Time Series Analysis

Product semantics translation from brain activity via adversarial learning

no code implementations29 Mar 2021 Pan Wang, Zhifeng Gong, Shuo Wang, Hao Dong, Jialu Fan, Ling Li, Peter Childs, Yike Guo

To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.

EEG Electroencephalogram (EEG) +1

Verifying Design through Generative Visualization of Neural Activities

no code implementations28 Mar 2021 Pan Wang, Danlin Peng, Simiao Yu, Chao Wu, Peter Childs, Yike Guo, Ling Li

A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.

EEG Electroencephalogram (EEG)

A General Framework for Revealing Human Mind with auto-encoding GANs

no code implementations10 Feb 2021 Pan Wang, Rui Zhou, Shuo Wang, Ling Li, Wenjia Bai, Jialu Fan, Chunlin Li, Peter Childs, Yike Guo

For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.

Brain Decoding

OmiEmbed: a unified multi-task deep learning framework for multi-omics data

1 code implementation3 Feb 2021 XiaoYu Zhang, Yuting Xing, Kai Sun, Yike Guo

To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules.

BIG-bench Machine Learning Decision Making +2

A Blockchain-based Trust System for Decentralised Applications: When trustless needs trust

no code implementations26 Jan 2021 Nguyen Truong, Gyu Myoung Lee, Kai Sun, Florian Guitton, Yike Guo

Blockchain technology has been envisaged to commence an era of decentralised applications and services (DApps) without the need for a trusted intermediary.

Cryptography and Security Distributed, Parallel, and Cluster Computing

Adversarially trained LSTMs on reduced order models of urban air pollution simulations

no code implementations5 Jan 2021 César Quilodrán-Casas, Rossella Arcucci, Christopher Pain, Yike Guo

This adversarially trained LSTM-based approach is used on the ROM in order to produce faster forecasts of the air pollution tracer.

Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective

no code implementations10 Nov 2020 Nguyen Truong, Kai Sun, Siyao Wang, Florian Guitton, Yike Guo

Furthermore, in the era of the Internet of Things and big data in which data is essentially distributed, transferring a vast amount of data to a data centre for processing seems to be a cumbersome solution.

Federated Learning Privacy Preserving

Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling

no code implementations26 Jun 2020 Chengliang Dai, Shuo Wang, Yuanhan Mo, Kaichen Zhou, Elsa Angelini, Yike Guo, Wenjia Bai

Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.

BIG-bench Machine Learning Image Segmentation +1

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

no code implementations23 Jun 2020 Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai

Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.

Image Segmentation MRI segmentation +1

An Epidemiological Modelling Approach for Covid19 via Data Assimilation

1 code implementation25 Apr 2020 Philip Nadler, Shuo Wang, Rossella Arcucci, Xian Yang, Yike Guo

We compare and discuss model results which conducts updates as new observations become available.

Efficient Deep Representation Learning by Adaptive Latent Space Sampling

no code implementations19 Mar 2020 Yuanhan Mo, Shuo Wang, Chengliang Dai, Rui Zhou, Zhongzhao Teng, Wenjia Bai, Yike Guo

Supervised deep learning requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.

General Classification Image Classification +2

Suggestive Labelling for Medical Image Analysis by Adaptive Latent Space Sampling

no code implementations MIDL 2019 Yuanhan Mo, Shuo Wang, Chengliang Dai, Zhongzhao Teng, Wenjia Bai, Yike Guo

Supervised deep learning for medical imaging analysis requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for medical segmentation tasks), which are expensive and time-consuming to obtain.


Regularizing Deep Multi-Task Networks using Orthogonal Gradients

1 code implementation14 Dec 2019 Mihai Suteu, Yike Guo

Deep neural networks are a promising approach towards multi-task learning because of their capability to leverage knowledge across domains and learn general purpose representations.

Multi-Task Learning

Biologically inspired architectures for sample-efficient deep reinforcement learning

no code implementations25 Nov 2019 Pierre H. Richemond, Arinbjörn Kolbeinsson, Yike Guo

Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation.

reinforcement-learning Reinforcement Learning (RL)

Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records

1 code implementation10 Nov 2019 Jingqing Zhang, Xiao-Yu Zhang, Kai Sun, Xian Yang, Chengliang Dai, Yike Guo

The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis.

How many weights are enough : can tensor factorization learn efficient policies ?

no code implementations25 Sep 2019 Pierre H. Richemond, Arinbjorn Kolbeinsson, Yike Guo

Deep reinforcement learning requires a heavy price in terms of sample efficiency and overparameterization in the neural networks used for function approximation.

reinforcement-learning Reinforcement Learning (RL)

Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification

4 code implementations17 Aug 2019 Xiao-Yu Zhang, Jingqing Zhang, Kai Sun, Xian Yang, Chengliang Dai, Yike Guo

The training procedure of OmiVAE is comprised of an unsupervised phase without the classifier and a supervised phase with the classifier.

Classification Decision Making +3

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

no code implementations5 Jul 2019 Wenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen E. Petersen, Yike Guo, Paul M. Matthews, Daniel Rueckert

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.

Image Segmentation Self-Supervised Learning +2

Static Activation Function Normalization

no code implementations3 May 2019 Pierre H. Richemond, Yike Guo

Recent seminal work at the intersection of deep neural networks practice and random matrix theory has linked the convergence speed and robustness of these networks with the combination of random weight initialization and nonlinear activation function in use.

Integrating Semantic Knowledge to Tackle Zero-shot Text Classification

2 code implementations NAACL 2019 Jingqing Zhang, Piyawat Lertvittayakumjorn, Yike Guo

Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification.

Data Augmentation General Classification +5

Combining learning rate decay and weight decay with complexity gradient descent - Part I

no code implementations7 Feb 2019 Pierre H. Richemond, Yike Guo

The role of $L^2$ regularization, in the specific case of deep neural networks rather than more traditional machine learning models, is still not fully elucidated.

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

1 code implementation13 Jun 2018 Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.

Traffic Prediction

Generative Creativity: Adversarial Learning for Bionic Design

no code implementations19 May 2018 Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo

Bionic design refers to an approach of generative creativity in which a target object (e. g. a floor lamp) is designed to contain features of biological source objects (e. g. flowers), resulting in creative biologically-inspired design.

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security

no code implementations20 Nov 2017 Hao Dong, Chao Wu, Zhen Wei, Yike Guo

However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction.

Anomaly Detection Decision Making +2

TensorLayer: A Versatile Library for Efficient Deep Learning Development

2 code implementations26 Jul 2017 Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo

Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others.


Semantic Image Synthesis via Adversarial Learning

2 code implementations ICCV 2017 Hao Dong, Simiao Yu, Chao Wu, Yike Guo

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e. g. intelligent image manipulation.

Image Generation Image Manipulation

Deep De-Aliasing for Fast Compressive Sensing MRI

no code implementations19 May 2017 Simiao Yu, Hao Dong, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo

Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.

Compressive Sensing De-aliasing +1

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

no code implementations10 May 2017 Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo

In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent.

Brain Tumor Segmentation Image Segmentation +1

The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation

no code implementations27 Mar 2017 Yuanhan Mo, Fangde Liu, Douglas McIlwraith, Guang Yang, Jingqing Zhang, Taigang He, Yike Guo

Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge.

Left Ventricle Segmentation

I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation

no code implementations20 Mar 2017 Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo

We demonstrate that %the capability of our method to understand the sentence descriptions, so as to I2T2I can generate better multi-categories images using MSCOCO than the state-of-the-art.

Data Augmentation Image Captioning +1

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

8 code implementations12 Mar 2017 Akara Supratak, Hao Dong, Chao Wu, Yike Guo

This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

EEG Electroencephalogram (EEG) +1

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

no code implementations10 Jan 2017 Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo

It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.

Translation Unsupervised Image-To-Image Translation

Mixed Neural Network Approach for Temporal Sleep Stage Classification

no code implementations15 Oct 2016 Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo

Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

Classification EEG +2

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

no code implementations5 Oct 2016 Orestis Tsinalis, Paul M. Matthews, Yike Guo, Stefanos Zafeiriou

We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge.

EEG Electroencephalogram (EEG)

DropNeuron: Simplifying the Structure of Deep Neural Networks

1 code implementation23 Jun 2016 Wei Pan, Hao Dong, Yike Guo

We proposed regularisers which support a simple mechanism of dropping neurons during a network training process.

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