Thursday, 13:30 - 15:00

Diffusion Imaging:

Diffusion Imaging:

0. Novel Bayesian Modeling for Dictionary Learning and Undersampled Reconstruction in Multishell HARDI

Kratika Gupta, IIT Bombay

Suyash Awate, University of Utah, USA

2. Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework

Chuyang Ye, Chinese Academy of Sciences

Large-scale data analysis:

4. A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies

Jenna Schabdach, University of Pittsburgh

William Wells, BWH Harvard, USA

Michael Cho, Harvard Medical School

Kayhan Batmanghelich*, University of Pittsburgh

Brain networks and Connectivity:

6. Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Nahuel Lascano, INRIA, France
Guillermo Gallardo, Université Côte d’Azur, Inria, France
Rachid Deriche, INRIA Sophia Antipolis, France
Dorian Mazauric, Université Côte d’Azur, Inria, France
Demian Wassermann, INRIA, France

8. Estimation of Brain Network Atlases using Diffusive-Shrinking Graphs: Application to Developing Brains

Islem Rekik, University of Dundee
Gang Li, University of North Carolina
Weili Lin, University of North Carolina
Dinggang Shen,

Tractography and Tract-based Statistics:

10. HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics

Jingwen Zhang, UNC Chapel Hill
Chao Huang, University of North Carolina at Chapel Hill
Rebecca Santelli, University of North Carolina at Chapel Hill
John Gilmore, University of North Carolina at Chapel Hill
Joseph Ibrahim, University of North Carolina at Chapel Hill
Hongtu Zhu, UNC Chapel Hill

Dynamic Functional Connectivity:

12. A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity

Yingying Zhu, UNC Chapel Hill
Guorong Wu, UNC Chapel Hill

Analysis on Manifolds:

14. Stochastic Development Regression on Non-Linear Manifolds

Line Kühnel, University of Copenhagen

Stefan Sommer, University of Copenhagen, Denmark

Disease Progression:

16. Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline

Jie Zhang, Arizona State University
Qingyang Li,
Richard Caselli,
Paul Thompson, USC
Jieping Ye,
Yalin Wang, Arizona State University

Model Understanding:

18. Quantifying the Uncertainty in Model Parameters using Gaussian Process based Markov Chain Monte Carlo: An Application to Cardiac Electrophysiological Models

Jwala Dhamala, RIT
John Sapp, Dalhousie University
Milan Horacek, Dalhousie University
Linwei Wang, Rochester Institute of Technology

20. Weakly supervised evidence pinpointing and description

Qiang Zhang, University of Warwick
Abhir Bhalerao, University of Warwick
Charles Hutchinson, University Hospitals Coventry and Warwickshire

Neural Networks / Deep Learning:

22. Risk Stratification of Lung nodules using 3D CNN based Multi-task learning

Sarfaraz Hussein, University of Central Florida
Kunlin Cao, CuraCloud Croporation
Qi Song, CuraCloud Corporation
Ulas Bagci, University of Central Florida

24. Modeling Task fMRI Data via Deep Convolutional Autoencoder

Heng Huang, Northwestern Polytechnical University
xintao hu, Northwestern Polytechnical University
Milad Makkie, University of Georgia
Qinglin Dong, University of Georgia
Yu Zhao, University of Georgia
Lei Guo, Northwestern Polytechnical University
Tianming Liu, University of Georgia

26. On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task

Wenqi Li, University College London
Guotai Wang, University College London
Lucas Fidon, University College London
Sebastian Ourselin, University College London, UK
M. Jorge Cardoso, University College London
Tom Vercauteren, University College London, UK

Alzheimer's Prognosis:

28. Predicting Interrelated Alzheimer's Disease Outcomes via New Self-Learned Structured Low-Rank Model

Xiaoqian Wang, Univ. of Texas at Arlington
Li Shen,
Heng Huang*,