Paper-and-Code

This repository is created for recording medical imaging & deep learning paper and code.

The files in the "Paper" folder are study papers from Prof. Huiguang He over the past five years. The files in the "Code" folder are codes connected with the papers of Prof. Huiguang He which were downloaded through github.com.

However, only part of the codes of papers of Prof. Huiguang He could be found and collected.

Here is the list of the papers with codes:

1. "Semi-supervised Bayesian Deep Multi-modal Emotion Recognition"(2017). Paper Code
2. "Sharing deep generative representation for perceived image reconstruction from human brain activity"(2017). Paper Code
3. "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge"(2018). Paper Code
4. "Reconstructing Perceived Images from Human Brain Activities with Bayesian Deep Multi-view Learning"(2018). Paper Code
5. "Multi-channel EEG recording during motor imagery of different joints from the same limb"(2020). Paper Code
6. "Structured Neural Decoding With Multitask Transfer Learning of Deep Neural Network Representations"(2020). Paper Code
7. "MS-MDA Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition"(2021). Paper Code

In README.md, papers from Prof. Huiguang He were classified according to the field of research of the paper.

There are three classifications: Medical Imaging, Deep Learning, and Multi-modality.

A. Medical Imaging:

(1) EEG

1."A prototype-based SPD matrix network for domain adaptation EEG emotion recognition"(2021)

2."MS-MDA Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition"(2021)

3."Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity"(2020)

4."Multi-channel EEG recording during motor imagery of different joints from the same limb"(2020)

5."Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition"(2020)

6."The Lasting Effects of Low-Frequency Repetitive Transcranial Magnetic Stimulation on Resting State EEG in Healthy Subjects"(2020)

7."EEG-Based Emotion Recognition with Prototype-Based Data Representation"(2019)

8."EEG-Based Emotion Recognition with Similarity Learning Network"(2019)

9."Multisource Transfer Learning for Cross- Subject EEG Emotion Recognition"(2019)

10."Predicting Epileptic Seizures from Intracranial EEG Using LSTM-Based Multi-task Learning"(2018)

(2) MRI

1."Multi-subject data augmentation for target subject semantic decoding with deep multi-view adversarial learning"(2021)

2."Reorganization of rich-clubs in functional brain networks during propofol-induced unconsciousness and natural sleep"(2020)

3."Transition and Dynamic Reconfiguration of Whole-Brain Network in Major Depressive Disorder"(2020)

4."Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children"(2018)

5."Comparison of NREM sleep and intravenous sedation through local information processing and whole brain network to explore the mechanism of general anesthesia"(2018)

6."Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge"(2018)

7."Multi-label Semantic Decoding from Human Brain Activity"2018

8."Alteration of gray matter texture features over the whole brain in medication-overuse headache using a 3-dimentional texture analysis"(2017)

9."Altered Spontaneous Brain Activity in Children with Early Tourette Syndrome a Resting-state fMRI Study"(2017)

10."Altered functional connectivity within and between the default model network and the visual network in primary open-angle glaucoma a resting-state fMRI study"(2017)

11."Diffusion Tractography and Graph Theory Analysis Reveal the Disrupted Rich-Club Organization of White Matter Structural Networks in Early Tourette Syndrome Children"(2017)

12."Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children"(2017)

13."Sharing deep generative representation for perceived image reconstruction from human brain activity"(2017)

14."The diagnostic value of high-frequency power-based diffusion-weighted imaging in prediction of neuroepithelial tumour grading"(2017)

(3) CT & X-ray

1."Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19"(2021)

2."3D Shape Reconstruction of Lumbar Vertebra From Two X-ray Images and a CT Model"(2020)

3."3D Shape Reconstruction of Lumbar Vertebra from Two X-ray Images and a CT Model"(2019)

(4) ERP

1."Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data"(2021)

B. Deep Learning:

(1) U-Net

1."Boundary Aware U-Net for Retinal Layers Segmentation in Optical Coherence Tomography Images(2021)"

2."CSU-Net A Context Spatial U-Net for Accurate Blood Vessel Segmentation in Fundus Images(2021)"

3."Dual Encoding U-Net for Retinal Vessel Segmentation"(2019)

(2) CNN

1."Multi-task contrastive learning for automatic CT and X-ray diagnosis of COVID-19"(2021)

2."A CNN -based comparing network for the detection of steady-state visual evoked potential responses"(2020)

3."A Transfer Learning Framework for RSVP-based Brain Computer Interface"(2020)

4."Conditional Generative Neural Decoding with Structured CNN Feature Prediction"(2020)

5."Deep Channel-Correlation Network for Motor Imagery Decoding From the Same Limb"(2020)

6."Reducing Calibration Efforts in RSVP Tasks With Multi-Source Adversarial Domain Adaptation"(2020)

7."Hierarchical Convolutional Neural Networks for EEG-Based Emotion Recognition"(2018)

8."Improving Image Classification Performance with Automatically Hierarchical Label Clustering"(2018)

(3) DNN

1."Dynamical Channel Pruning by Conditional Accuracy Change for Deep Neural Networks"(2020)

2."Structured Neural Decoding With Multitask Transfer Learning of Deep Neural Network Representations"(2020)

3."Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models"(2019)

4."Learning What and Where An Interpretable Neural Encoding Model"(2019)

5."Reconstructing Perceived Images From Human Brain Activities With Bayesian Deep Multiview Learning"(2019)

6."Reconstructing Perceived Images from Human Brain Activities with Bayesian Deep Multi-view Learning"(2018)

(4) FCN(Fully Convolutional Network)

1."Automatic brain labeling via multi-atlas guided fully convolutional networks"(2019)

(5) RNN

1."Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks"(2018)

C. Multi-modality:

1."Multimodal deep generative adversarial models for scalable doubly semi-supervised learning"(2021)

2."Semi-supervised cross-modal image generation with generative adversarial networks"(2020)

3."Doubly Semi-supervised Multimodal Adversarial Learning for Classification, Generation and Retrieval"(2019)

4."Semi-supervised Deep Generative Modelling of Incomplete Multi-Modality Emotional Data"(2018)

5."Multi-modal multiple kernel learning for accurate identification of Tourette syndrome children"(2017)

6."Multi-threshold White Matter Structural Networks Fusion for Accurate Diagnosis of Tourette Syndrome Children"(2017)

7."Semi-supervised Bayesian Deep Multi-modal Emotion Recognition"(2017)