8:30 – 9:00 Registration, speaker check-in and poster setup
9:00 – 9:15 Opening Remarks
9:15 – 10:30 Morning Session 1: Plenary Talk
Title: Large-Scale Structured Sparse Learning for Biomedical Data Analysis
by Dr. Heng Huang, Professor, Department of Computer Science and Engineering, University of Texas, Arlington.
Abstract: Sparsity is one of the intrinsic properties of real-world data, thus the sparse learning has recently emerged as a powerful tool to obtain models of high-dimensional data with high degree of interpretability at low computational cost, and provide great opportunities to analyze the big, complex, and diverse datasets. By enforcing properly designed structured sparsity, we can integrate the specific data/feature structures and domain knowledge into the machine learning models to simplify data models and discover predictive patterns in biomedical data analytics. Data science research is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment and prevention of diseases, including the recently announced BRAIN (Brain Research through Advancing Innovative Neurotechnologies) and Precision Medicine Initiatives. To address the challenging problems in current biomedical data analysis, I will introduce several newly developed large-scale structured sparse learning models for multi-dimensional data integration, heterogeneous multi-task learning, group/graph structured data analysis, longitudinal feature learning, etc. I will also show how to utilize these structured sparsity learning algorithms to solve various biomedical applications.

10:30-11:00 Coffee break
11:00-12:30 Morning Session 2
Session Chair: Prof. Anne L Martel

  • [MLMI-O-1]
    Segmentation of Right Ventricle in Cardiac MR Images using Shape Regression
    Suman Sedai, Pallab Roy, and Rahil Granavi
  • [MLMI-O-2]
    Visual Saliency Based Active Learning for Prostate MRI Segmentation
    Dwarikanath Mahapatra, and Joachim M. Buhmann
  • [MLMI-O-3]
    Soft-Split Random Forest for Anatomy Labeling
    Guangkai Ma, Yaozong Gao, Li Wang, Ligang Wu, and Dinggang Shen
  • [MLMI-O-4]
    A New Image Data Set and Benchmark for Cervical Dysplasia Classification Evaluation
    Tao Xu, Cheng Xin, L. R. Long, S. Antani, Z. Xue, E. Kim, and Xiaolei Huang
  • [MLMI-O-5]
    Machine Learning on High Dimensional Shape Data from Subcortical Brain Surfaces: A Comparison of Feature Selection and Classification Methods
    Benjamin Wade, Shantanu Joshi, Boris Gutman, and Paul Thompson

12:30 – 13:45 Lunch & Posters

  • [MLMI-P-1]
    Predicting Standard-dose PET Image from Low-dose PET and Multimodal MR Images Using Mapping-based Sparse Representation
    Yan Wang, Pei Zhang, Le An, Guangkai Ma, Jiayin Kang, Xi Wu, Jiliu Zhou, David Lalush, Weili Lin, and Dinggang Shen
  • [MLMI-P-2]
    Boosting Convolutional Filters with Entropy Sampling For Optic Cup And Disc Image Segmentation From Fundus Images
    Julian G. Zilly, Joachim M. Buhmann, Dwarikanath Mahapatra
  • [MLMI-P-3]
    Brain Fiber Clustering Using Non Negative Kernelized Matching Pursuit
    Kuldeep Kumar, Christian Desrosiers, and Kaleem Siddiqi
  • [MLMI-P-4]
    Automatic Detection of Good/Bad Colonies of iPS Cells Using Local Features
    Atsuki Masuda, Bisser Raytchev, Takio Kurita, Toru Imamura, Masashi Suzuki, Toru Tamaki, and Kazufumi Kaneda
  • [MLMI-P-5]
    Detecting Abnormal Cell Division Patterns in Early Stage Human Embryo Development
    Aisha Khan, Stephen Gould, and Mathieu Salzmann
  • [MLMI-P-6]
    Identification of Infants at Risk for Autism Using Multi-Parameter Hierarchical White Matter Connectomes
    Yan Jin, Chong-Yaw Wee, Feng Shi, Kim-Han Thung, Pew-Thian Yap, and Dinggang Shen
  • [MLMI-P-7]
    Group-constrained Laplacian Eigenmaps: Longitudinal AD Biomarker Learning
    Ricardo Guerrero, Christian Ledig, Alexander Schmidt-Richberg, and Daniel Rueckert
  • [MLMI-P-8]
    Multi-Atlas Context Forests for Knee MR Image Segmentation
    Qin Liu, Qian Wang, Lichi Wang, Yaozong Gao, and Dinggang Shen
  • [MLMI-P-9]
    Longitudinal Patch-Based Segmentation of Multiple Sclerosis White Matter Lesions
    Snehashis Roy, Aaron Carass, Jerry Prince, and Dzung Pham
  • [MLMI-P-10]
    Hierarchical Multi-modal Image Registration by Learning Common Feature Representations
    Hongkun Ge, Guorong Wu, Li Wang, Yaozong Gao, and Dinggang Shen
  • [MLMI-P-11]
    Semi-automatic Liver Tumour Segmentation in Dynamic Contrast-enhanced CT Scans Using Random Forests and Supervoxels
    Pierre-Henri Conze, François Rousseau, Vincent Noblet, Fabrice Heitz, Riccardo Memeo, and Patrick Pessaux
  • [MLMI-P-12]
    Flexible and Latent Structured Output Learning: Application to Histology
    Gustavo Carneiro, Tingying Peng, Christine Bayer, and Nassir Navab
  • [MLMI-P-13]
    Identifying Abnormal Network Alterations Common to Traumatic Brain Injury and Alzheimer’s Disease Patients Using Functional Connectome Data
    Brent Munsell, Davy Vanderweyen, Jacobo Mintzer, Olga Mintzer, Andy Gajadhar, Xun Zhu, Guorong Wu, and Jane Joseph
  • [MLMI-P-14]
    Multimodal Multi-label Transfer Learning for Early Diagnosis of Alzheimer’s Disease
    Bo Cheng, Mingxia Liu, and Daoqiang Zhang
  • [MLMI-P-15]
    Soft-split Sparse Regression Based Radom Forest for Predicting Future Clinical Scores of Alzheimer’s Disease
    Lei Huang, Yaozong Gao, Yan Jin, Kim-Han Thung, and Dinggang Shen
  • [MLMI-P-16]
    Multi-View Classification for Identification of Alzheimer’s Disease
    Xiaofeng Zhu, Heung-Il Suk, Guorong Wu, Kim-Han Thung, Yue Gao, and Dinggang Shen
  • [MLMI-P-17]
    Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology
    Mohammad Peikari, Judit Zubovits, Gina Clarke, and Anne L. Martel
  • [MLMI-P-18]
    A Composite of Features for Learning-based Coronary Artery Segmentation on Cardiac CT Angiography
    Yanling Chi, Weimin Huang, Jiayin Zhou, Liang Zhong, Swee Yaw Tan, Keng Yung Jih Felix, Low Choon Seng Sheon, Ru San Tan
  • [MLMI-P-19]
    Ensemble Prostate Tumor Classification in H&E Whole Slide Imaging via Stain Normalization and Cell Density Estimation
    Michaela Weingant, Hayley M. Reynolds, Annette Haworth, Catherine Mitchell, Scott Williams, and Matthew D. DiFranco
  • [MLMI-P-20]
    Computer-assisted Diagnosis of Lung Cancer Using Topological and Local Features
    Jiawen Yao, Dheeraj Ganti, Xin Luo, Guanghua Xiao, Yang Xie, Shirley Yan, and Junzhou Huang
  • [MLMI-P-21]
    Inherent Structure-guided Multi-view Learning for Alzheimer’s Disease and Mild Cognitive Impairment Classification
    Mingxia Liu, Daoqiang Zhang, and Dinggang Shen
  • [MLMI-P-22]
    Nonlinear Feature Transformation and Deep Fusion for Alzheimer’s Disease Staging Analysis
    Yani Chen, Bibo Shi, Charles D. Smith, and Jundong Liu
  • [MLMI-P-23]
    Tumor Classification by Deep Polynomial Network and Multiple Kernel Learning on Small Ultrasound Image Dataset
    Xiao Liu, Jun Shi, and Qi Zhang
  • [MLMI-P-24]
    Multi-Source Information Gain for Random Forest: An Application to CT Image Prediction from MRI Data
    Tri Huynh, Yaozong Gao, Jiayin Kang, Pei Zhang, and Dinggang Shen
  • [MLMI-P-25]
    Joint Learning of Multiple Longitudinal Prediction Models by Exploring Internal Relations
    Baiying Lei, Siping Chen, Dong Ni, and Tianfu Wang

13:45 – 15:30 Afternoon Session 1
Session Chair: Prof. Kenji SuzuKi

  • [MLMI-O-6]
    Node-based Gaussian Graphical Model for Identifying Discriminative Brain Regions from Connectivity Graphs
    Bernard Ng, Anna Milazzo, and Andre Altmann
  • [MLMI-O-7]
    BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease
    Mohammad Khatami, Tobias Schmidt-Wilcke, Pia Sundgren, Amin Abbasloo, Bernhard Schölkopf, and Thomas Schultz
  • [MLMI-O-8]
    FADR: Functional-Anatomical Discriminative Regions for Rest fMRI Characterization
    Marta Nuñez, Sonja Simpraga, Maria Ángeles Jurado, Maite Garolera, Roser Pueyo, and Laura Igual
  • [MLMI-O-9]
    Craniomaxillofacial Deformity Correction via Sparse Representation in Coherent Space
    Zuoyong Li, Le An, Jun Zhang, Li Wang, James Xia, and Dinggang Shen
  • [MLMI-O-10]
    Nonlinear Graph Fusion for Multi-Modal Classification of Alzheimer’s Disease
    Tong Tong, Katherine Gray, Qinquan Gao, Liang Chen, and Daniel Rueckert
  • [MLMI-O-11]
    HEp-2 Staining Pattern Recognition Using Stacked Fisher Network for Encoding Weber Local Descriptor
    Xian-Hua Han, Yen-Wei Chen, and Gang Xu

15:30- 16:00 Coffee break
16:00 – 17:15 Afternoon Session 2
Session Chair: Prof. Gary E. Christensen

  • [MLMI-O-12]
    Supervoxel Classification Forests for Estimating Pairwise Image Correspondences
    Fahdi Kanavati, Tong Tong, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, and Ben Glocker
  • [MLMI-O-13]
    Non-rigid Free-form 2D-3D Registration Using Statistical Deformation Model
    Guoyan Zheng, and Weimin Yu
  • [MLMI-O-14]
    Learning and Combining Image Similarities for Neonatal Brain Population Studies
    Veronika Zimmer, Ben Glocker, Paul Aljabar, Serena Counsell, Mary Rutherford, A David Edwards, Jo Hajnal, Miguel Ángel González Ballester, Daniel Rueckert, and Gemma Piella
  • [MLMI-O-15]
    Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images
    Noel Codella, Junjie Cai, Mani Abedini, Rahil Garnavi, Alan Halpern, and John Smith

 17:15 – 17:30 Closing remarks (Best paper(s) will be announced)