September, 24th, 2019

Our paper on pnemothorax screening based on CT images, "Deep Learning-Enabled System for Rapid Pneumothorax Screening on Chest CT", has been accepted by European Journal of Radiology. Link can be found here.

In this work we aim at speeding up and re-prioritizing the radiology workflow using deep learning, taking advantage of its speed and high diagnostic positive detection in analyzing medical images such as CT in pneumothorax. We further conducted large-scale inter-observational study for evaluating radiologist performance of pneumothorax diagnosis, and investigated how AI and human performed differently.

August, 20th, 2019

Two of our abstracts are accepted by American Heart Association 2019 annual meeting:

"Personalized Treatment for Heart Failure With Preserved Ejection Fraction Using Deep Reinforcement Learning" is accepted as oral presentation, using reinforcement learning to optimize the treatment plans of HFpEF patients.

and "Recurrent Neural Network Enhance Phenotyping in Heart Failure With Preserved Ejection Fraction Using Electronic Health Record" is accepted as poster presentation, using recurrent neural network to discover HFpEF phenotypes from patients' longitudinal EHR data.

June, 10th, 2019

This year Quanzheng Li and I from MGH/HMS, Richard Leahy from USC and Bin Dong from PKU are organising the first MICCAI workshop on Multiscale Multimodal Medical Imaging (MMMI 2019). If you work on related areas, we are looking forward to your paper submission. The submission deadline is July 31th 2019.

May, 27th, 2019

Our paper pulmonary lymph node metastasis modeling using network inference, entitled "Transition Patterns between N1 and N2 Stations Discovered from Data-driven Lymphatic Metastasis Study in Non-Small Cell Lung Cancer", is accepted by 2019 World Conference on Lung Cancer for oral presentation.

This work is based on our own network inference modeling method "PathInf", whicn is online at arXiv

May, 11th, 2019

Our paper on spatio-temporal modeling of fMRI data, "4D Modeling of fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)", is accepted by IEEE Transactions on Cognitive and Developmental Systems. The link can be found here

This work is an extension of our previous conference presentation at MICCAI 2018, "Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)".

January, 2nd, 2019

Our paper on multi-modal image fusing in a deep learning context, "Deep Learning-based Image Segmentation on Multi-modal Medical Imaging", is accepted by IEEE Transactions on Radiation and Plasma Medical Sciences. The link can be found here

This work is an extension of our previous conference presentation at ISBI 2018, "Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes".

December, 25th, 2018

Our review paper on functional neuroimaging, "Functional Neuroimaging in the New Era of Big Data", is accepted by Genomics Proteomics and Bioinformatics.

This work also summarizes my major perspective on the future trend of functional neuroimaging, proposed in my PhD thesis.

December, 18th, 2018

Four papers accepted by ISBI 2019:

"Multi-Size Computer-Aided Diagnosis of Positron Emission Tomography Images Using Graph Convolutional Networks"

This is our first attempt in using graph representation for medical image analysis, trying to tackle the challenge of varying image sizes in deep learning inputs. We found that graph convolution neural networks, which is based on the concept of non-Euclidean convolution operations, achieves superior performance comparing with traditional 3D CNNs.

"3D Regional Shape Analysis of Left Ventricle Using MR Images: Abnormal Myocardium Detection and Classification"

This work summarizes our preliminary results in performing 3D shape analysis of Cardiac MR images, showing that shape spectral analysis based using multilinear principal component analysis can obtain group-wise shape-based image marker for classifying heart failure vs. normal controls, and different heart failure subtypes.

"Novel Radiomic Features Based on Graph Theory for PET Image Analysis"

In this work we developed new graph-based radiomics features, which capture better intratumoral heterogeneity from PET images comparing with traditional, image-derived texture features.

"Automated Segmentation of Cervical Nuclei in Pap Smear Images using Deformable Multi-path Ensemble Model"

In this collaborative work with PKU, we developed the Deformable Multipath Ensemble Model (D-MEM) based on an ensembling scheme. It achieved state-of-the-art accuracy for nuclei segmentation in pap smear images for cervical cancer screening. Presentation slides of it could be found here

October, 1st, 2018

Our work on network inference is now online at arXiv

The PathInf program developed in this work can infer most possible transition paths among variables, only with independently observed instances of a transition process with large missing values. The program is tested on simulation data as well as lung caner metastasis data on lung lymph nodes. Performance of PathInf is compared with commonly applied GES (Greedy Equivalence Search) method, showing better recovery accuracy, especially for its capability of reducing false positive edges caused by the missing values.

August, 1st, 2018

Our left ventricle quantification paper is accepted by STACOM workshop of MICCAI 2018:

"Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning", link can be found here arXiv link can be found here

June, 15th, 2018

Our Neuroimage paper "Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs" is covered by Editorial of Dialogues in Clinical Neuroscience

The article, "New ways of understanding brain neurocircuitry", can be found here

May, 25th, 2018

Two papers accepted by MICCAI 2018:

"RBC Semantic Segmentation For Sickle Cell Disease Based on Deformable U-Net", arXiv link can be found here


"Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)", which is our collaboration work with Dr. Yu Zhao, a PhD student of Prof. Tianming Liu at the University of Georgia. arXiv link can be found here

May, 8th, 2018

Our presentation in ARRS 2018 is covered by auntminnie, the report can be found here

March, 30th, 2018

Our project on pneumothorax detection is among the four finalist of the 2018 NVIDIA Global Impact Award, detailed information can be found here

February, 25th, 2018

Our paper on distributed analytics for fMRI based on rank-1 decomposition and cloud computing, titled "A Distributed Computing Platform for fMRI Big Data Analytics", has been accepted by IEEE Transactions on Big Data, the link can be found here

February, 21th, 2018

Two reviews on our JACR paper can be found at Radiology Business and auntminnie.

February, 5th, 2018

Our perspective paper on the impact of artificial intelligence on radiology is now online at:Journal of the American College of Radiology, the link can be found here

It has been selected as the Continuing Medical Education (CME) material of the month, for ACR credentials.

Janurary, 22th, 2018

Tensorflow codes for the 2D and 3D deformable convolution network, associated with our work on blood cell semantic segmentation, is now available at my GitHub page.

December, 22th, 2017

Our work on multi-modal image fusion analysis has been accepted by ISBI 2018:

"Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes".

which can also be found on arXiv

November, 20th, 2017

Our work on using deep learning for pneumothorax detection on chest CT images has been accepted by American Roentgen Ray Society 2018 as oral presentation:

"Deep Learning Algorithm for rapid automatic detection of pneumothorax on chest CT"

November, 14th, 2017

Another paper on functional brain dynamics has been accepted by Neuroimage:

In this work we modeled the fMRI signal using dynamic functional networks based on sliding time window approach, then further analyze the patterns of the networks using time-evolving graphs.

November, 1st, 2017

Our work on fusing multi-modal medical images can be found at arXiv

We discussed three different fusion schemes for performing supervised learning on medical image analysis. This is a preliminary study from us on how to utilize images from different modalties together to make more accurate and robust image-based decision.

October, 23th, 2017

Our work on using deformable U-Net for sickle cell segmentation and classification can be found at arXiv

The collaboration work with Mo Zhang at Peking University and Mengjia Xu at Northeastern University develop a fully automatic framework for simutaenous cell segmentation and classification (sementic segmentation) on red blood cells microscopic images.

July, 18th, 2017

Two papers accepted by MLMI 2017:

"Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis", arXiv link here


"Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis", arXiv link here

May, 29th, 2017

Our work on tensor decomposition on sparse and low rank data can be found at arXiv

The collaboration work with Songting Shi at Peking University offers an integrated framework to perform PARAFAC tensor decomposition on large-scale data with the propertity of both sparse and low-rank.

Feburary, 28th, 2017

Our work on using dicitonary learning for fMRI signal de-noising is accepted by OHBM 2017

"FMRI Signal Denoising by Dictionary Learning for High-Resolution Functional Connectivity Inference"

The MATLAB package for de-noising (both dictionary learning-based and non-local-mean-based) will be publicalby available soon.

January, 8th, 2017

Two papers accepted by ISBI 2017:

"Template-guided Functional Network Identification via Supervised Dictionary Learning"

This work is based on the r1DL model previously proposed in my KDD2016 paper. It has shown fast and accurate performance for identifying target functional networks given a set of pre-defined templates from fMRI signals.

"Exploring Human Brain Activation via Nested Sparse Coding and Functional Operators"

The collaboration work with Shu Zhang, a PhD student of Prof. Tianming Liu, proposed a highly novel framework which characterizes functional networks as the results of "functional operators" inside the brain, which offers a brand new perspective for brain functional decoding.

October, 13th, 2016

Invited talk on the International Workshop on Big Data Neuroimaging Analytics for Brain and Mental Health at the 2016 International Conference on Brain Informatics and Health.

September, 14th, 2016

Joined the Clinical Data Science Center at Harvard Medical School and Massachusetts General Hospital as postdoctoral research fellow, under the mentorship of James H. Thrall, MD and Dr. Quanzheng Li.

June 2nd, 2016

Two papers accepted by MICCAI 2016:

"Modeling Functional Dynamics of Cortical Gyri and Sulci"

This work is our preliminary attempt in investigating the differences of functional dynamics between gyri and sulci areas, which are supposed to work in different roles within the brain functional architecture.

"Discover Mouse Gene Coexpression Landscape Using Dictionary Learning and Sparse Coding"

The work from Yujie Li using sparse coding method on the mouse brain gene data obtained surprisingly cleared-edge map of the mouse brain regions.

May 12th, 2016

Awarded the Outstanding Graduate Dissertation/Thesis! Thanks to Computer Science Department for the recognition and my advisor, Prof. Tianming Liu, for the mentorship!

May 11th, 2016

Paper accepted by ACM SigKDD 2016:

"Scalable Fast Rank-1 Dictionary Learning for fMRI Big Data Analysis"

The collaboration work across our group, Dr. Shannon Quinn and Dr. Jieping Ye features our solutions towards fMRI big data analysis by leveraging the distributed computation power by Python Spark.

March, 17th, 2016

Talk on SIAM-SEAS session "Parallel and distributed computing for biomedical imaging". The presentation slide could be found here

December 23, 2015

Three papers accepted by ISBI 2016:

"Modeling Functional Network Dynamics Via Multi-Scale Dictionary Learning and Network Continuums" (with Dr. Jieping Ye)

This work marks the development of "network continnum", a novel concept characterizing the continuous/disruptive dynamics of the functional networks. The model reveals ever-changing spatial patterns of the same networks overtime.

"Multiple-Demand System Identification and Characterization Via Sparse Representations of fMRI Data"

This work investigates the well-known multiple-demand system through a network decomposition approach. The work is in sync with our previous Human Brain Mapping paper "Sparse representation of HCP grayordinate data reveals novel functional architecture of cerebral cortex" which focused on MDS on grayordinates (i.e. cortical surface).

"Identifying Group-Wise Consistent Sub-Networks Via Spatial Sparse Representation of Natural Stimulus fMRI Data" (collaboration work with Cheng Lyu)

This work offers a great tool for the brain network analysis from based on its spatial distribution, including sub-network identification and calculating similarities among different networks.

October 14th, 2015

"Sparse representation of HCP grayordinate data reveals novel functional architecture of cerebral cortex", (collaboration work with Xi Jiang) has been accepted by Human Brain Mapping.

July, 20th, 2015

Starting visit at Prof. Jieping Ye's group at University of Michigan.

April, 1st, 2015

Our article, " Holistic Atlases of Functional Networks and Interactions Reveal Reciprocal Organizational Architecture of Cortical Function", has been selected as the feature story of IEEE Transactions on Biomedical Engineering.

March, 17th, 2015

Guest lecture on UGA CSCI6900: Mining Massive Datasets (at Dr. Shannon Quinn's invitation), the presentation slide could be found here

February, 13th, 2015

Thanks to Franklin Foundation for providing the travel award to support me for attending ISBI 2015!

February, 5th, 2015

Two papers accepted by ISBI 2015:
"Interactive Exemplar-Based Segmentation Toolkit for Biomedical Image Analysis" (with Dr. Hanchuan Peng)
"Characterizing and Differentiating Task-Based and Resting State FMRI Signals Via Two-Stage Dictionary Learning"

February, 3rd, 2015

"Characterizing and Differentiating Task-based and Resting State FMRI Signals via Two-stage Sparse Representations" (collaboration work with Shu Zhang) has been accepted by Brain Imaging and Behavior

September, 29th, 2014

Starting a 3-months visit at Allen Institute of Brain Science, with Zhi Zhou, Brain Long, Hanbo Chen (also from UGA) and Hanchuan Peng.

September, 29th, 2014

"Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models" (collaboration work with Dr. Jinli Ou and Prof. Leo Xie at ZJU) has been accepted by Brain Topography.

May, 13th, 2014

Oral presentation at ISBI 2014, on the topics of Bayesian network-based change point detection and sparse representation of functional network using DICCCOL.

April, 15th, 2013

Nominated for the best student paper award of ISBI 2013

Reach me at:

25 New Chardon St, 449A, Boston, MA, 02114


My Google Scholar page

My ORCID page


My Publons page


My arXiv author id page

Homepage of the Center for Advanced Medical Computing and Analysis (CAMCA) at HMS/MGH, where I'm currently working at.

Homepage of the Cortical Architecture Imaging and Discovery (CAID) lab, where I previous joined at UGA computer science department.

Homepage of my previous collaboration work with Dr. Joe Tsien at GRU, the Brain Decoding Project.

Homepage of the 3D visualization platform I've been previously working at, the Vaa3D system by Dr. Hanchuan Peng.