June 7th, 2021

Our work on the co-modeling of brain iron deposition and gene expression patterns in Alzheimer’s disease patients have been accepted by Frontiers in Human Neuroscience. Its link can be found here (open access).

In this work, by combining AD progression-related regions identified from SWI imaging with gene expression data from Allen Brain Atlas, we observed considerable overlap between these two modalities. Further, we have identified a new potential AD-related gene (MEF2C) closely related to the interaction between iron deposition and AD progression in the brain.


March 23rd, 2021

Our work on severe outcome prediction and a clinical risk score (CO-RISK score) system for COVID-19 patient triage at emergency department is now online at arXiv.

In this study we constructed the "MGB Cohort", a database covering all patients suspected of COVID-19 presented to the emergency department at the four hospital sites of the MGB system. A total of 11,060 patients were used in the model development and validation, according to our inclusion/exclusion criteria. A deep learning system based on the architecture of Deep and Cross network was developed to predict the patient's outcome in 24/72 hours based on the EHR and imaging (CXR) data up to the initial present to the emergency department.


February 21st, 2021

Awarded as distinguished reviewer by IEEE TMI:


February 1st, 2021

Our work on analyzing chest radiograph of COVID-19 patients using deep metric learning, "Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19", has been accepted by Medical Image Analysis. Its link can be found here, its pdf can be downloaded here.

In this work, Aoxiao Zhong, a PhD student at Harvard SEAS and I together developed a deep metric learning model based on a contrastive learning scheme with attention module, in order to perform chest radiograph image retrieval by a query image from incoming patients. By doing so, we can perform diagnosis of chest radiograph based on the labels of retrieved images, as well as visual comparisons by the physicians of the images between query and retrieved images. The whole framework is currently undergoing the preliminary deployment stage to the clinical workflow of MGB system.


October 30th, 2020

Our work on modeling multi-label objects for image analysis, "Multi-label Detection and Classification of Red Blood Cells in Microscopic Images", has been accepted by The 7th Big Data Analytic Technology For Bioinformatics and Health Informatics Workshop (KDDBHI 2020), its link can be found here, its pdf can be downloaded here.

This work was developed by my mentored trainees, Wei Qiu and Jiaming Guo, who were visiting undergraduate students at MGH. The model aims at solving multi-label (e.g. multiple type of deformed cells) problem in medical image semantic segmentation.


June 23rd, 2020

Our work on deformable U-Net, validated on the RBC segmentation task, "Automated Semantic Segmentation of Red Blood Cells for Sickle Cell Disease", has been accepted by IEEE Journal of Biomedical and Health Informatics, its link can be found here, its pdf can be downloaded here.

This work is an extension of our previous conference presentation at MICCAI 2018, "RBC Semantic Segmentation For Sickle Cell Disease Based on Deformable U-Net".


June 22nd, 2020

Two papers accepted by MICCAI 2020:

"Discovering Functional Brain Networks with 3D Residual Autoencoder (ResAE)", its link can be found here, its pdf can be downloaded here.

and

"Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification", its link can be found here, its pdf can be downloaded here.

Both of the works are in collaboration with Dr. Qinglin Dong, a PhD student of Prof. Tianming Liu at the University of Georgia.


April 7th, 2020

Our paper, "ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning", has been awarded by ISBI 2020 as the 2nd place winner of Best Paper Award

Congratulates to Mo Zhang, my mentored PhD student as the first author of this paper!

The announcement video can be found here.

Link to this paper be found on here, its pdf can be downloaded here.


January 17th, 2020

Our work on fMRI single denoising using dictionary learning method, "Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study", has been accepted by IEEE Access.

The link can be found here (open access).

This work is an extension of our previous conference presentation at MLMI 2017, "Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis". In this collaboration work with Dr. Seongah Jeong, a research fellow at Harvard SEAS, we developed a dictionary learning-based method to perform signal denoising on fMRI data, and revealed enhanced activation patterns and functional connectivities from the denoised data.


January 7th, 2020

Our work on adaptive scale deep learning method has been accepted by ISBI 2020:

"ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning".

Its link be found on here, its pdf can be downloaded here.


December 24th, 2019

Our review paper on functional neuroimaging, "Functional Neuroimaging in the New Era of Big Data", is accepted by Genomics, Proteomics and Bioinformatics. The link can be found here, its pdf can be downloaded here.

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


November 5th, 2019

Our paper on PET image analysis using graph convolution network (GCN), entitled "Predicting Alzheimer’s Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging", has been accepted by the IEEE BigData 2019 Workshop of Deep Graph Learning (DGLMA'19). Its link can be found here, its pdf can be downloaded here.

This work was developed by my mentored trainees, Wei Qiu and Jiaming Guo, who were visiting undergraduate students at MGH. It is among the first works leveraging graph representation for analyzing medical imaging data.


October 7th, 2019

Our pneumothorax screening paper, "Deep Learning-Enabled System for Rapid Pneumothorax Screening on Chest CT", is covered by The Imaging Wire, link can be found here


September 24th, 2019

Our paper on pneumothorax screening based on CT images, "Deep Learning-Enabled System for Rapid Pneumothorax Screening on Chest CT", has been accepted by European Journal of Radiology. Its link can be found here, its pdf can be downloaded 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 have been 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. Abstract as appeared in the Circulation journal can be found here

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. Abstract as appeared in the Circulation journal can be found here


August 1st, 2019

Promoted to Instructor at Department of Radiology, Harvard Medical School and Massachusetts General Hospital.


June 10th, 2019

https://mmmi2019.github.io/


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.
Proceeding of MMMI 2019 as part of the MICCAI 2019 conference proceeding, published as Lecture Notes in Computer Science (LNCS) book series, is now available online at Springer. Cover of this proceeding can be downloaded here.


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.

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 cancer 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.


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, its pdf can be downloaded 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, its pdf can be downloaded 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 18th, 2018

Four papers accepted by ISBI 2019:

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

This is developed by my mentored trainee, Xuandong Zhao, who was a visiting undergraduate students at MGH. It 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. Its link can be found here, its pdf can be downloaded here.

"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. Its link can be found here, its pdf can be downloaded here.

"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. Its link can be found here, its pdf can be downloaded here.

"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. Its link can be found here, its pdf can be downloaded here. Presentation slides of it could be found here


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", its link can be found here, its pdf can be downloaded here. This work is later included in the paper "Left Ventricle Quantification Challenge: A Comprehensive Comparison and Evaluation of Segmentation and Regression for Mid-ventricular Short-axis Cardiac MR Data" (co-authored), its 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", its link can be found here. The journal version of this paper can be found here, pdf of the journal paper can be downloaded here.

In this collaboration work with Mo Zhang at Peking University and Mengjia Xu at Northeastern University, we developed a fully automatic framework for simultaneous cell segmentation and classification (semantic segmentation) on red blood cells microscopic images.

and:

"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. Its link can be found here. The journal version of this paper can be found here, pdf of the journal paper can be downloaded here.


May 8th, 2018

Our presentation on pneumothorax detection in American Roentgen Ray Society (ARRS) 2018 Annual Meeting 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, its pdf can be downloaded 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, its pdf can be downloaded here.

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


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".

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 modalities together to make more accurate and robust image-based decision.

The link to its journal version can be found here, its pdf can be downloaded here.


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, "Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs", has been accepted by Neuroimage, its link can be found here, pdf can be downloaded here.

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.


July 18th, 2017

Two papers accepted by MLMI 2017:

"Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis", its link can be found here, its pdf can be downloaded here.

In this work we proposed the self-paced learning method to generate virtual training samples for supervised learning, particularly to overcome the sample size limitations in medical image analysis.

and:

"Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis", its link can be found here. The journal version of this work can be found here, its pdf can be downloaded here.


May 29th, 2017

Our work on tensor decomposition on sparse and low rank data is online 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 property of both sparse and low-rank.


February 28th, 2017

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

"FMRI Signal Denoising by Dictionary Learning for High-Resolution Functional Connectivity Inference", its link can be found here.


January 8th, 2017

Two papers accepted by ISBI 2017:

"Template-guided Functional Network Identification via Supervised Dictionary Learning". Its link can be found here, its pdf can be downloaded here.

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.

and:

"Exploring Human Brain Activation via Nested Sparse Coding and Functional Operators". Its link can be found here, its pdf can be downloaded here.

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 Harvard Medical School and Massachusetts General Hospital, as well as MGH & BWH Center for Clinical Data Science (CCDS), 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". Its link can be found here, its pdf can be downloaded here.

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 a sparse coding method on the mouse brain gene data obtained a surprisingly cleared-edge map of the mouse brain regions. Its link can be found here. Journal version of this work can be found here, pdf can be downloaded here.


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". Its link can be found here, pdf can be downloaded here.

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)
and
"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, for my work "Discovering Common Functional Connectomics Signatures". Its oral presentation video could be found here.

Reach me at:

25 New Chardon St, 449A, Boston, MA, 02114
xiangli.shaun{at}gmail.com

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ORCID iD iconorcid.org/0000-0002-9851-6376

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