no code implementations • 19 Jun 2024 • Apoorva Safai, Erin Jonaitis, Rebecca E Langhough, William R Buckingham, Sterling C. Johnson, W. Ryan Powell, Amy J. H. Kind, Barbara B. Bendlin, Pallavi Tiwari
Morphological Similarity Networks (MSN) were constructed for each participant based on the similarity in distribution of cortical thickness of brain regions, followed by computation of local and global network features.
1 code implementation • 25 Jan 2023 • Zhijian Yang, Junhao Wen, Ahmed Abdulkadir, Yuhan Cui, Guray Erus, Elizabeth Mamourian, Randa Melhem, Dhivya Srinivasan, Sindhuja T. Govindarajan, Jiong Chen, Mohamad Habes, Colin L. Masters, Paul Maruff, Jurgen Fripp, Luigi Ferrucci, Marilyn S. Albert, Sterling C. Johnson, John C. Morris, Pamela Lamontagne, Daniel S. Marcus, Tammie L. S. Benzinger, David A. Wolk, Li Shen, Jingxuan Bao, Susan M. Resnick, Haochang Shou, Ilya M. Nasrallah, Christos Davatzikos
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases.
no code implementations • 11 Oct 2020 • Vishnu M. Bashyam, Jimit Doshi, Guray Erus, Dhivya Srinivasan, Ahmed Abdulkadir, Mohamad Habes, Yong Fan, Colin L. Masters, Paul Maruff, Chuanjun Zhuo, Henry Völzke, Sterling C. Johnson, Jurgen Fripp, Nikolaos Koutsouleris, Theodore D. Satterthwaite, Daniel H. Wolf, Raquel E. Gur, Ruben C. Gur, John C. Morris, Marilyn S. Albert, Hans J. Grabe, Susan M. Resnick, R. Nick Bryan, David A. Wolk, Haochang Shou, Ilya M. Nasrallah, Christos Davatzikos
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine.
no code implementations • CVPR 2017 • Won Hwa Kim, Mona Jalal, Seongjae Hwang, Sterling C. Johnson, Vikas Singh
The adoption of "human-in-the-loop" paradigms in computer vision and machine learning is leading to various applications where the actual data acquisition (e. g., human supervision) and the underlying inference algorithms are closely interwined.
1 code implementation • ICCV 2019 • Haoliang Sun, Ronak Mehta, Hao H. Zhou, Zhichun Huang, Sterling C. Johnson, Vivek Prabhakaran, Vikas Singh
Motivated by developments in modality transfer in vision, we study the generation of certain types of PET images from MRI data.
no code implementations • 20 Nov 2017 • Ronak Mehta, Hyunwoo J. Kim, Shulei Wang, Sterling C. Johnson, Ming Yuan, Vikas Singh
Recent results in coupled or temporal graphical models offer schemes for estimating the relationship structure between features when the data come from related (but distinct) longitudinal sources.
1 code implementation • ICML 2017 • Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh
Many studies in biomedical and health sciences involve small sample sizes due to logistic or financial constraints.
no code implementations • CVPR 2017 • Hyunwoo J. Kim, Nagesh Adluru, Heemanshu Suri, Baba C. Vemuri, Sterling C. Johnson, Vikas Singh
Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging.
no code implementations • CVPR 2017 • Vamsi K. Ithapu, Risi Kondor, Sterling C. Johnson, Vikas Singh
Multiresolution analysis and matrix factorization are foundational tools in computer vision.
no code implementations • 4 Mar 2017 • Felipe Gutierrez-Barragan, Vamsi K. Ithapu, Chris Hinrichs, Camille Maumet, Sterling C. Johnson, Thomas E. Nichols, Vikas Singh, the ADNI
We find that RapidPT achieves its best runtime performance on medium sized datasets ($50 \leq n \leq 200$), with speedups of 1. 5x - 38x (vs. SnPM13) and 20x-1000x (vs. NaivePT).
no code implementations • NeurIPS 2016 • Hao Zhou, Vamsi K. Ithapu, Sathya Narayanan Ravi, Vikas Singh, Grace Wahba, Sterling C. Johnson
Consider samples from two different data sources $\{\mathbf{x_s^i}\} \sim P_{\rm source}$ and $\{\mathbf{x_t^i}\} \sim P_{\rm target}$.
no code implementations • CVPR 2016 • Seong Jae Hwang, Nagesh Adluru, Maxwell D. Collins, Sathya N. Ravi, Barbara B. Bendlin, Sterling C. Johnson, Vikas Singh
There is a great deal of interest in using large scale brain imaging studies to understand how brain connectivity evolves over time for an individual and how it varies over different levels/quantiles of cognitive function.
no code implementations • ICCV 2015 • Seong Jae Hwang, Maxwell D. Collins, Sathya N. Ravi, Vamsi K. Ithapu, Nagesh Adluru, Sterling C. Johnson, Vikas Singh
Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation.
no code implementations • ICCV 2015 • Won Hwa Kim, Sathya N. Ravi, Sterling C. Johnson, Ozioma C. Okonkwo, Vikas Singh
A variety of studies in neuroscience/neuroimaging seek to perform statistical inference on the acquired brain image scans for diagnosis as well as understanding the pathological manifestation of diseases.
no code implementations • CVPR 2015 • Won Hwa Kim, Barbara B. Bendlin, Moo. K. Chung, Sterling C. Johnson, Vikas Singh
Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience.
no code implementations • NeurIPS 2013 • Chris Hinrichs, Vamsi K. Ithapu, Qinyuan Sun, Sterling C. Johnson, Vikas Singh
In this paper, we show that permutation testing in fact amounts to populating the columns of a very large matrix ${\bf P}$.
no code implementations • CVPR 2014 • Hyunwoo J. Kim, Nagesh Adluru, Maxwell D. Collins, Moo. K. Chung, Barbara B. Bendlin, Sterling C. Johnson, Richard J. Davidson, Vikas Singh
Linear regression is a parametric model which is ubiquitous in scientific analysis.