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Qingyu Zhao | Adolf Pfefferbaum | Ehsan Adeli | Greg Zaharchuk | Edith V Sullivan | Kilian M Pohl | Jiahong Ouyang
[1] Jun Wang,et al. Deep Multi-modal Latent Representation Learning for Automated Dementia Diagnosis , 2019, MICCAI.
[2] Daoqiang Zhang,et al. Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment , 2015, Brain Imaging and Behavior.
[3] Phillip Isola,et al. Contrastive Multiview Coding , 2019, ECCV.
[4] V Dalca Adrian,et al. Unsupervised Deep Learning for Bayesian Brain MRI Segmentation , 2019 .
[5] Seong-Whan Lee,et al. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis , 2014, NeuroImage.
[6] Stephan Günnemann,et al. Diffusion Improves Graph Learning , 2019, NeurIPS.
[7] Xiahai Zhuang,et al. Diagnosis of Alzheimer’s Disease via Multi-Modality 3D Convolutional Neural Network , 2019, Front. Neurosci..
[8] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[9] Kilian M. Pohl,et al. Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs , 2021, IEEE Journal of Biomedical and Health Informatics.
[10] Dinggang Shen,et al. Latent Representation Learning for Alzheimer’s Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data , 2019, IEEE Transactions on Medical Imaging.
[11] Kaveh Hassani,et al. Contrastive Multi-View Representation Learning on Graphs , 2020, ICML.
[12] Silvio Savarese,et al. Social LSTM: Human Trajectory Prediction in Crowded Spaces , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Silvio Savarese,et al. Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[14] M. Toepper,et al. Dissociating Normal Aging from Alzheimer’s Disease: A View from Cognitive Neuroscience , 2017, Journal of Alzheimer's disease : JAD.
[15] Mert R. Sabuncu,et al. An Unsupervised Learning Model for Deformable Medical Image Registration , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[16] Jie Tang,et al. Self-Supervised Learning: Generative or Contrastive , 2020, IEEE Transactions on Knowledge and Data Engineering.
[17] Yong Fan,et al. Non-rigid image registration using self-supervised fully convolutional networks without training data , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[18] Oriol Vinyals,et al. Representation Learning with Contrastive Predictive Coding , 2018, ArXiv.
[19] Geoffrey E. Hinton,et al. A Simple Framework for Contrastive Learning of Visual Representations , 2020, ICML.
[20] Kilian M. Pohl,et al. Longitudinal self-supervised learning , 2021, Medical Image Anal..
[21] Di Guo,et al. Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment , 2018, Front. Neurosci..
[22] Snehashis Roy,et al. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge , 2017, NeuroImage.
[23] Mert R. Sabuncu,et al. Unsupervised deep learning for Bayesian brain MRI segmentation , 2019, MICCAI.
[24] Lloyd T. Elliott,et al. Brain aging comprises many modes of structural and functional change with distinct genetic and biophysical associations , 2019, bioRxiv.
[25] Daniel Rueckert,et al. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease , 2012, NeuroImage.
[26] Tanya P. Garcia,et al. Statistical Approaches to Longitudinal Data Analysis in Neurodegenerative Diseases: Huntington’s Disease as a Model , 2017, Current Neurology and Neuroscience Reports.
[27] Alzheimer's Disease Neuroimaging Initiative,et al. Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers , 2020, Brain Imaging and Behavior.
[28] Kilian M. Pohl,et al. LSSL: Longitudinal Self-Supervised Learning , 2020, ArXiv.
[29] Kaiming He,et al. Momentum Contrast for Unsupervised Visual Representation Learning , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Manhua Liu,et al. RNN-based longitudinal analysis for diagnosis of Alzheimer's disease , 2019, Comput. Medical Imaging Graph..
[31] J. Whitwell,et al. Longitudinal imaging: change and causality , 2008, Current opinion in neurology.
[32] Ehsan Adeli,et al. Self-Supervised Representation Learning via Neighborhood-Relational Encoding , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[33] Dinggang Shen,et al. Landmark‐based deep multi‐instance learning for brain disease diagnosis , 2018, Medical Image Anal..
[34] Mert R. Sabuncu,et al. VoxelMorph: A Learning Framework for Deformable Medical Image Registration , 2018, IEEE Transactions on Medical Imaging.
[35] Alexander Kolesnikov,et al. Revisiting Self-Supervised Visual Representation Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Julien Mairal,et al. Unsupervised Learning of Visual Features by Contrasting Cluster Assignments , 2020, NeurIPS.