Program

8:00 – 8:30 Registration, speaker check-in and poster setup
8:30 – 8:45 Opening Remarks
8:45 – 10:00 Morning Session 1: Plenary Talk
“Sparse Learning for Large-Scale Biomedical Data”
Dr. Jieping Ye, Associate Professor, Department of Computer Science and Engineering, Arizona State University.
Abstract: Sparse methods have been shown to be a versatile and powerful tool for biomedical data analysis. In this talk, we consider sparse methods for (1) biomarker identification from very high-dimensional biomedical data, (2) feature extraction from biomedical images, (3) identification of hierarchical interactions (of variables) from biomedical data. We address the computational challenges by designing novel optimization strategies which scale sparse methods to large-size problems.
10:00-10:30 Coffee break
10:30-12:00 Morning Session 2: Image Segmentation and Registration
Session Chair: Pierrick Coupé
• [MLMI-O-1] 3D Intervertebral Disc Localization through Representation Learning with Knowledge Transfer
Cheng Chen, D. Belavy, Guoyan Zheng
• [MLMI-O-2] Graph-Based Label Propagation in Fetal Brain MR Images
Lisa Koch, Robert Wright, Deniz Vatansever, Venessa Kyriakopoulou, Christina Malamateniou, Prachi Patkee, Mary Rutherford, Joseph Hajnal, Paul Aljabar, Daniel Rueckert
• [MLMI-O-3] Deep Learning Based Automatic immune Cell Detection for Immunohistochemistry Images
Ting Chen, Christophe Chefd’hotel
• [MLMI-O-4] Stacked Multiscale Feature learning for Domain Independent Medical Image Segmentation
Ryan Kiros, Karteek Popuri, Dana Cobzas, Martin Jagersand
• [MLMI-O-5] Detection of Mammographic Masses by Content-Based Image Retrieval
Menglin Jiang, Shaoting Zhang, Dimitris Metaxas
12:00 – 13:15 Lunch & Posters
• [MLMI-P-1] Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer
Hao Chen, Dong Ni, Xin Yang, Pheng Ann Heng
• [MLMI-P-2] Hierarchical Learning of Atlas Forests for Automatic Labeling of MR Brain Images
Lichi Zhang, Qian Wang, Yaozong Gao, Guorong Wu, Dinggang Shen
• [MLMI-P-3] Anatomically Constrained Week Classifier Fusion for Early Detection of Alzheimer’s Disease
Mawulawoe Komlagan, Vinh-Thong Ta, Xingyu Pan, Jean-Philippe Domenger, Louis Collins, Pierrick Coupé
• [MLMI-P-4] Automatic Bone and Marrow Extraction from Dual Energy CT through SVM Margin-Based Multi-Material Decomposition Model Selection
Harini Veeraraghavan, Duc Fehr, Ross Schmidtlein, Sinchun Hwang, Joseph Deasy
• [MLMI-P-5] Sparse Discriminative Feature Selection for Multi-Class Alzheimer’s Disease Classification
Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen
• [MLMI-P-6] Multi-Atlas Segmentation with Learning-Based Label Fusion
Hongzhi Wang
• [MLMI-P-7] Optimal MAP Parameters Estimation in STAPLE-Learning from Performance Parameters versus Image Similarity Information
Subrahmanyam Gorthi, Alireza Akhondi-Asl, Simon Warfield
• [MLMI-P-8] Solutions for Missing Parameters in Computer-Aided Diagnosis with Multiparametric Imaging Data
Hussam Al-Deen Ashab, Piotr Kozlowski, Mehdi Moradi
• [MLMI-P-9] Novel Multi-Atlas Segmentation by Matrix Completion
Gerard Sanroma, Guorong Wu, Kim-Ham Thung, Dinggang Shen
• [MLMI-P-10] Network Guided Group Feature Selection for Classification of Autism Spectrum Disorder
Veronika Cheplygina, David Tax, Marco Loog, Aasa Feragen
• [MLMI-P-11] Deformation Field Correction for Spatial Normalization of PET Images Using a Population-derived Partial Least Squares Model
Murat Bilgel, Aaron Carass, Susan Resnick, Dean Wong, Jerry Prince
• [MLMI-P-12] Persistent Reeb Graph Matching for Fast Brain Search
Yonggang Shi, Junning Li, Arthur Toga
• [MLMI-P-13] Structured Random Forest for Myocardium Delineation in 3D Echocardiography
João Domingos, Richard Stebbing, Paul Leeson, Alison Noble
• [MLMI-P-14] Improved Reproducibility of Neuroanatomical Definition through Diffeomorphometry and Complexity Reduction
Daniel Tward, Jorge Jovicich, Andrea Soricelli, Giovanni Frisoni, Alian Trouve, Laurent Younes, Michael Miller
• [MLMI-P-15] Topological Descriptors of Histology Images
Nikhil Singh, Heather Couture, J. S. Marron, Charles Perou, Marc Niethammer
• [MLMI-P-16] Robust Deep Learning for Improved Classification of AD/MCI Patients
Feng Li, Loc Tran, Kim-Ham Thung, Shuiwang Ji, Dinggang Shen, Jiang Li
• [MLMI-P-17] Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation
Snehashis Roy, Aaron Carass, Jerry Prince, Dzung Pham
• [MLMI-P-18] Context-aware Anatomical Landmark Detection: Application to Deformable Model Initialization in Prostate CT Images
Yaozong Gao, Dinggang Shen
• [MLMI-P-19] Interactive Prostate Segmentation based on Adaptive Feature Selection and Manifold Regularization
Sanghyun Park, Yaozong Gao, Yinghuan Shi, Dinggang Shen
• [MLMI-P-20] Feature Selection Based on SVM Significance Maps for Classification of Dementia
Esther Bron, Marion Smits, John van Swieten, Wiro Niessen, Stefan Klein
• [MLMI-P-21] Prediction of Standard-dose PET Image by Low-dose PET and MRI Images
Jiayin Kang, Yaozong Gao, Yao Wu, Guangkai Ma, Feng Shi, Weili Lin, Dinggang Shen
• [MLMI-P-22] Searching for Structures of Interest in an Ultrasound Video Sequence
Mohammad Ali Maraci, Raffaele Napolitano, Aris Papageorghiou, Alison Noble
• [MLMI-P-23] Online Discriminative Multi-Atlas Learning with Application to Isointense Infant Brain Segmentation
Xuchu Wang, Li Wang, Heung-Il Suk, Dinggang Shen
• [MLMI-P-24] Colon Biopsy Classification Using Crypt Architecture
Assaf Cohen, Ehud Rivlin, Ilan Schimshoni, Edmond Sabo
• [MLMI-P-25] Sparsity-Learning-Based Longitudinal MR Image Registration for Early Brain Development
Qian Wang, Guorong Wu, Li Wang, Weili Lin, and Dinggang Shen

13:15 – 15:00 Afternoon Session 1: Classification/Regression
Session Chair: Alison Noble
• [MLMI-O-6] Inferring Sources of Dementia Progression with Network Diffusion Model
Chenhui Hu, Xue Hua, Paul Thompson, Georges Fakhri, Quanzheng Li
• [MLMI-O-7] In Vivo MRI based Prostate Cancer Identification with Random Forests and Auto-context Model
Chunjun Qian, Li Wang, Ambereen Yousuf, Aytekin Oto, Dinggang Shen
• [MLMI-O-8] Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification
Jiangjia Zhang, Luping Zhou, Lei Wang, Wangqiang Li
• [MLMI-O-9] Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression
Zhen Yang, Shenghua Zhong, Aaron Carass, Sarah Ying, Jerry Prince
• [MLMI-O-10] Manifold Alignment and Transfer Learning for Classification of Alzheimer’s Disease
Ricardo Cuerrero, Christian Ledig, Daniel Ruckert
• [MLMI-O-11] Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images
Yaozong Gao, Li Wang, Yeqin Shao, Dinggang Shen
15:00- 15:30 Coffee break
15:30 – 16:45 Afternoon Session 2: Computer-aided Detection/Diagnosis
Session Chair: Kenji Suzuki
• [MLMI-O-12] Gleason Grading of Prostate Tumors with Max-Margin Conditional Random Fields
Joseph Jacobs, Eleftheria Panagiotaki, Daniel Alexander
• [MLMI-O-13] Geodesic Geometric Mean of Regional Covariance Descriptors as an Image-Level Descriptor for Nuclear Atypia Grading in Breast Histography Images
Adnan Khan, Korsuk Sirinukunwattana, Nasir Rajpoot
• [MLMI-O-14] A Constrained Regression Forests Solution to 3D Fetal Ultrasound Plane Localization for Longitudinal Analysis of Brain Growth and Maturation
Mohammad Yaqub, Anil Kopuri, Sylvia Rueda, Kenny McCormick, Peter Sullivan, Alison Noble
• [MLMI-O-15] Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation
Youngjin Yoo, Tom Brosch, Anthony Traboulsee, David Li, Roger Tam
16:45 – 17:00 Closing remarks

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