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Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis.

, , , , and . IEEE Trans. Med. Imaging, 35 (12): 2524-2533 (2016)

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GRAND: Unbiased Connectome Atlas of Brain Network by Groupwise Graph Shrinkage and Network Diffusion., , , and . CNI@MICCAI, volume 11083 of Lecture Notes in Computer Science, page 127-135. Springer, (2018)Identifying Abnormal Network Alterations Common to Traumatic Brain Injury and Alzheimer's Disease Patients Using Functional Connectome Data., , , , , , , and . MLMI, volume 9352 of Lecture Notes in Computer Science, page 229-237. Springer, (2015)Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data., , , , , , , , , and . NeuroImage, (2015)Evaluating Shape Correspondence for Statistical Shape Analysis: A Benchmark Study., , and . IEEE Trans. Pattern Anal. Mach. Intell., 30 (11): 2023-2039 (2008)White matter connectomes at birth accurately predict cognitive abilities at age 2., , , , , , and . NeuroImage, (2019)Pre-organizing Shape Instances for Landmark-Based Shape Correspondence., , , and . Int. J. Comput. Vis., 97 (2): 210-228 (2012)Editorial: Deep Mining Big Social Data., , , and . World Wide Web, 21 (6): 1449-1452 (2018)Fast multiple shape correspondence by pre-organizing shape instances., , and . CVPR, page 840-847. IEEE Computer Society, (2009)Detecting Anatomical Landmarks for Fast Alzheimer's Disease Diagnosis., , , , and . IEEE Trans. Med. Imaging, 35 (12): 2524-2533 (2016)Domain Transfer Learning for MCI Conversion Prediction., , , , and . IEEE Trans. Biomed. Eng., 62 (7): 1805-1817 (2015)