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ORCID3rd SASHIMI@MICCAI 2018: Granada, Spain
- Ali Gooya
, Orcun Goksel, Ipek Oguz, Ninon Burgos:
Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Lecture Notes in Computer Science 11037, Springer 2018, ISBN 978-3-030-00535-1 - Hoo-Chang Shin, Neil A. Tenenholtz, Jameson K. Rogers, Christopher G. Schwarz, Matthew L. Senjem, Jeffrey L. Gunter, Katherine P. Andriole, Mark Michalski:
Medical Image Synthesis for Data Augmentation and Anonymization Using Generative Adversarial Networks. 1-11 - Saba Momeni, Amir Fazlollahi, Pierrick Bourgeat, Parnesh Raniga, Paul A. Yates, Nawaf Yassi, Patricia M. Desmond, Jurgen Fripp, Yongsheng Gao, Olivier Salvado:
Data Augmentation Using Synthetic Lesions Improves Machine Learning Detection of Microbleeds from MRI. 12-19 - Blake E. Dewey, Can Zhao, Aaron Carass, Jiwon Oh, Peter A. Calabresi, Peter C. M. van Zijl, Jerry L. Prince:
Deep Harmonization of Inconsistent MR Data for Consistent Volume Segmentation. 20-30 - Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Aaron Carass, Jerry L. Prince, Nobuhiko Sugano, Yoshinobu Sato:
Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN - Effects of Gradient Consistency Loss and Training Data Size. 31-41 - Emmanuel Vallée, Wenchuan Wu, Francesca Galassi, Saâd Jbabdi, Stephen M. Smith:
A Machine Learning Approach to Diffusion MRI Partial Volume Estimation. 42-51 - Chengjia Wang, Gillian Macnaught, Giorgos Papanastasiou, Tom J. MacGillivray, David E. Newby:
Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Consistency Networks. 52-60 - Kerstin Kläser, Pawel Markiewicz, Marta Bianca Maria Ranzini, Wenqi Li, Marc Modat, Brian F. Hutton, David Atkinson, Kris Thielemans, M. Jorge Cardoso, Sébastien Ourselin:
Deep Boosted Regression for MR to CT Synthesis. 61-70 - Igor Peterlík, David Svoboda, Vladimír Ulman, Dmitry V. Sorokin, Martin Maska:
Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motile Cells with Filopodia. 71-79 - Apoorva Sikka, Skand Vishwanath Peri, Deepti R. Bathula:
MRI to FDG-PET: Cross-Modal Synthesis Using 3D U-Net for Multi-modal Alzheimer's Classification. 80-89 - David Svoboda, Tereza Necasová, Lenka Tesarová, Pavel Simara:
Tubular Network Formation Process Using 3D Cellular Potts Model. 90-99 - Sunghee Jung, Soochahn Lee, Byunghwan Jeon, Yeonggul Jang, Hyuk-Jae Chang:
Deep Learning Based Coronary Artery Motion Artifact Compensation Using Style-Transfer Synthesis in CT Images. 100-110 - Dário Augusto Borges Oliveira, Matheus Palhares Viana:
Lung Nodule Synthesis Using CNN-Based Latent Data Representation. 111-118 - Raghav Mehta, Tal Arbel:
RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours. 119-129 - Nathan Olliverre, Guang Yang, Gregory G. Slabaugh, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso:
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-linear and Deep Learning Models. 130-138
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