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ORCID8th MLMI@MICCAI 2017: Quebec City, QC, Canada
- Qian Wang, Yinghuan Shi, Heung-Il Suk, Kenji Suzuki
:
Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings. Lecture Notes in Computer Science 10541, Springer 2017, ISBN 978-3-319-67388-2 - Marie Bieth, Esther Alberts, Markus Schwaiger, Bjoern H. Menze:
From Large to Small Organ Segmentation in CT Using Regional Context. 1-9 - Sylvester Chiang, Sharmila Balasingham, Lara Richmond, Belinda Curpen, Mia Skarpathiotakis, Anne L. Martel:
Motion Corruption Detection in Breast DCE-MRI. 10-18 - Jirí Borovec, Jan Kybic, Rodrigo Nava:
Detection and Localization of Drosophila Egg Chambers in Microscopy Images. 19-26 - Felix Durlak, Michael Wels, Chris Schwemmer, Michael Sühling, Stefan Steidl, Andreas K. Maier:
Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring. 27-35 - Boris Kodner, Shiri Gordon, Jacob Goldberger, Tammy Riklin Raviv:
Atlas of Classifiers for Brain MRI Segmentation. 36-44 - Seongah Jeong, Xiang Li, Jiarui Yang, Quanzheng Li, Vahid Tarokh:
Dictionary Learning and Sparse Coding-Based Denoising for High-Resolution Task Functional Connectivity MRI Analysis. 45-52 - Jorge Samper-González, Ninon Burgos, Sabrina Fontanella, Hugo Bertin, Marie Odile Habert, Stanley Durrleman, Theodoros Evgeniou, Olivier Colliot:
Yet Another ADNI Machine Learning Paper? Paving the Way Towards Fully-Reproducible Research on Classification of Alzheimer's Disease. 53-60 - Darko Stern, Philipp Kainz, Christian Payer, Martin Urschler:
Multi-factorial Age Estimation from Skeletal and Dental MRI Volumes. 61-69 - Anees Kazi, Shadi Albarqouni, Amelia Jiménez-Sánchez, Sonja Kirchhoff, Peter Biberthaler, Nassir Navab, Diana Mateus:
Automatic Classification of Proximal Femur Fractures Based on Attention Models. 70-78 - Fahdi Kanavati, Kazunari Misawa, Michitaka Fujiwara, Kensaku Mori, Daniel Rueckert, Ben Glocker:
Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation. 79-87 - Yani Chen, Bibo Shi, Zhewei Wang, Tao Sun, Charles D. Smith, Jundong Liu:
Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble. 88-96 - Yao Xiao, Ajay Gupta, Pina C. Sanelli, Ruogu Fang:
STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion. 97-105 - Danni Cheng, Manhua Liu:
Classification of Alzheimer's Disease by Cascaded Convolutional Neural Networks Using PET Images. 106-113 - Yuru Pei, Yunai Yi, Gengyu Ma, Yuke Guo, Gui Chen, Tianmin Xu, Hongbin Zha:
Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images. 114-122 - Yuru Pei, Haifang Qin, Gengyu Ma, Yuke Guo, Gui Chen, Tianmin Xu, Hongbin Zha:
Multi-scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base. 123-131 - Tao Zhou, Kim-Han Thung, Xiaofeng Zhu, Dinggang Shen:
Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-status Dementia Diagnosis. 132-140 - Dakai Jin, Ziyue Xu, Adam P. Harrison, Kevin George, Daniel J. Mollura:
3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels. 141-149 - Pei Dong, Xiaohuan Cao, Jun Zhang, Minjeong Kim, Guorong Wu, Dinggang Shen:
Efficient Groupwise Registration for Brain MRI by Fast Initialization. 150-158 - Jun Wang, Qian Wang, Shitong Wang, Dinggang Shen:
Sparse Multi-view Task-Centralized Learning for ASD Diagnosis. 159-167 - Yu Zhang, Han Zhang, Xiaobo Chen, Mingxia Liu, Xiaofeng Zhu, Dinggang Shen:
Inter-subject Similarity Guided Brain Network Modeling for MCI Diagnosis. 168-175 - Alborz Amir-Khalili, Soheil Kianzad, Rafeef Abugharbieh, Ivan Beschastnikh:
Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data. 176-184 - Siqi Liu, Donghao Zhang, Yang Song, Hanchuan Peng, Weidong Cai:
Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images. 185-193 - Junyi Yan, Yu Meng, Gang Li, Weili Lin, Dazhe Zhao, Dinggang Shen:
Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity. 194-202 - Baris U. Oguz, Russell T. Shinohara, Paul A. Yushkevich, Ipek Oguz:
Gradient Boosted Trees for Corrective Learning. 203-211 - Xiang Li, Aoxiao Zhong, Ming Lin, Ning Guo, Mu Sun, Arkadiusz Sitek, Jieping Ye, James Thrall, Quanzheng Li:
Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis. 212-219 - Jinquan Sun, Yinghuan Shi, Yang Gao, Dinggang Shen:
A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling. 220-228 - Mohammad Arafat Hussain, Alborz Amir-Khalili, Ghassan Hamarneh, Rafeef Abugharbieh:
Collage CNN for Renal Cell Carcinoma Detection from CT. 229-237 - Zhen Yu, Xudong Jiang, Tianfu Wang, Bai Ying Lei:
Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images. 238-246 - Christopher P. Bridge, Christos Ioannou, J. Alison Noble:
Localizing Cardiac Structures in Fetal Heart Ultrasound Video. 247-255 - Enzo Ferrante, Puneet Kumar Dokania, Rafael Marini, Nikos Paragios:
Deformable Registration Through Learning of Context-Specific Metric Aggregation. 256-265 - Dong Nie, Li Wang, Roger Trullo, Jianfu Li, Peng Yuan, James J. Xia, Dinggang Shen:
Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-Learning Based Cascade Framework. 266-273 - Guodong Zeng, Xin Yang, Jing Li, Lequan Yu, Pheng-Ann Heng, Guoyan Zheng:
3D U-net with Multi-level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images. 274-282 - Luca Minciullo, Paul A. Bromiley, David T. Felson, Timothy F. Cootes:
Indecisive Trees for Classification and Prediction of Knee Osteoarthritis. 283-290 - Can Zhao, Aaron Carass, Junghoon Lee, Yufan He, Jerry L. Prince:
Whole Brain Segmentation and Labeling from CT Using Synthetic MR Images. 291-298 - Yang Li, Jingyu Liu, Mei-Lin Luo, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen:
Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification. 299-306 - Yang Li, Jingyu Liu, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen:
Fusion of High-Order and Low-Order Effective Connectivity Networks for MCI Classification. 307-315 - Yang Li, Hao Yang, Ke Li, Pew-Thian Yap, Minjeong Kim, Chong-Yaw Wee, Dinggang Shen:
Novel Effective Connectivity Network Inference for MCI Identification. 316-324 - Zeju Li, Yuanyuan Wang, Jinhua Yu:
Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network. 325-333 - Kenji Suzuki, Junchi Liu, Amin Zarshenas, Toru Higaki, Wataru Fukumoto, Kazuo Awai:
Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to "Virtual" High-Dose CT Images. 334-343 - Giles Tetteh, Markus Rempfler, Claus Zimmer, Bjoern H. Menze:
Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction. 344-352 - Daniel Moyer, Boris A. Gutman, Neda Jahanshad, Paul M. Thompson:
Product Space Decompositions for Continuous Representations of Brain Connectivity. 353-361 - Nicha C. Dvornek, Pamela Ventola, Kevin A. Pelphrey, James S. Duncan:
Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks. 362-370 - Dmitry Petrov, Boris A. Gutman, Shih-Hua (Julie) Yu, Kathryn I. Alpert, Artemis Zavaliangos-Petropulu, Dmitry Isaev, Jessica A. Turner, Theo G. M. van Erp, Lei Wang, Lianne Schmaal, Dick J. Veltman, Paul M. Thompson:
Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging. 371-378 - Seyed Sadegh Mohseni Salehi, Deniz Erdogmus, Ali Gholipour:
Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks. 379-387
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