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ORCIDMLCN@MICCAI 2021: Strasbourg, France
- Ahmed Abdulkadir
, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal M. W. Tax, Thomas Wolfers:
Machine Learning in Clinical Neuroimaging - 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings. Lecture Notes in Computer Science 13001, Springer 2021, ISBN 978-3-030-87585-5
Computational Anatomy
- Sadhana Ravikumar, Laura E. M. Wisse, Sydney Lim, David J. Irwin, Ranjit Ittyerah, Long Xie, Sandhitsu R. Das, Edward B. Lee, M. Dylan Tisdall, Karthik Prabhakaran, John A. Detre, Gabor Mizsei, John Q. Trojanowski, John L. Robinson, Theresa Schuck, Murray Grossman, Emilio Artacho-Pérula, Maria Mercedes Iñiguez de Onzoño Martin, María del Mar Arroyo Jiménez, Mónica Muñoz López, Francisco Javier Molina Romero, Maria del Pilar Marcos Rabal, Sandra Cebada Sánchez, José Carlos Delgado González, Carlos de la Rosa-Prieto, Marta Córcoles Parada, David A. Wolk, Ricardo Insausti, Paul A. Yushkevich:
Unfolding the Medial Temporal Lobe Cortex to Characterize Neurodegeneration Due to Alzheimer's Disease Pathology Using Ex vivo Imaging. 3-12 - Mariana da Silva, Carole H. Sudre, Kara Garcia, Cher Bass, M. Jorge Cardoso, Emma C. Robinson:
Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks. 13-22 - Matthias Wilms, Pauline Mouches, Jordan J. Bannister, Deepthi Rajashekar, Sönke Langner, Nils D. Forkert:
Towards Self-explainable Classifiers and Regressors in Neuroimaging with Normalizing Flows. 23-33 - Verónica Muñoz Ramírez, Nicolas Pinon, Florence Forbes, Carole Lartizien, Michel Dojat:
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients. 34-43 - Malte Klingenberg, Didem Stark, Fabian Eitel, Kerstin Ritter:
MRI Image Registration Considerably Improves CNN-Based Disease Classification. 44-52 - Zhiwei Deng, Jiong Zhang, Yonggang Shi:
Dynamic Sub-graph Learning for Patch-Based Cortical Folding Classification. 53-62 - Louise Guillon, Bastien Cagna, Benoit Dufumier, Joël Chavas, Denis Rivière, Jean-François Mangin:
Detection of Abnormal Folding Patterns with Unsupervised Deep Generative Models. 63-72 - Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary:
PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction. 73-81 - Wenting Duan, Lei Zhang, Jordan Colman, Giosue Gulli, Xujiong Ye:
Multi-modal Brain Segmentation Using Hyper-Fused Convolutional Neural Network. 82-91 - Yuanfang Qiao, Haoyi Tao, Jiayu Huo, Wenjun Shen, Qian Wang, Lichi Zhang:
Robust Hydrocephalus Brain Segmentation via Globally and Locally Spatial Guidance. 92-100
Brain Networks and Time Series
- Logan Z. J. Williams, Abdulah Fawaz, Matthew F. Glasser, A. David Edwards, Emma C. Robinson:
Geometric Deep Learning of the Human Connectome Project Multimodal Cortical Parcellation. 103-112 - Kai-Cheng Chuang, Sreekrishna Ramakrishnapillai, Lydia Bazzano, Owen T. Carmichael:
Deep Stacking Networks for Conditional Nonlinear Granger Causal Modeling of fMRI Data. 113-124 - Ahmed El Gazzar, Rajat Mani Thomas, Guido van Wingen:
Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling. 125-134 - Yang Ji, Samuel Deslauriers-Gauthier, Rachid Deriche:
Structure-Function Mapping via Graph Neural Networks. 135-144 - Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson:
Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity. 145-154 - Ne Yang, Xiong Xiao, Xianyu Wang, Guocan Gu, Liwei Zhang, Hongen Liao:
H3K27M Mutations Prediction for Brainstem Gliomas Based on Diffusion Radiomics Learning. 155-164 - Sreekrishna Ramakrishnapillai, Harris R. Lieberman, Jennifer C. Rood, Stefan M. Pasiakos, Kori Murray, Preetham Shankapal, Owen T. Carmichael:
Constrained Learning of Task-Related and Spatially-Coherent Dictionaries from Task fMRI Data. 165-173
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