22 citations · 55 across the 6 of their papers we have counts for
6 papers
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
Ming Bo Cai, Michael Shvartsman, Anqi Wu +2
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent ye…
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers
Apoorv Vyas, Nataraj Jammalamadaka, Xia Zhu +3
As deep learning methods form a critical part in commercially important applications such as autonomous driving and medical diagnostics, it is important to reliably detect out-of-d…
Temporo-Spatial Collaborative Filtering for Parameter Estimation in Noisy DCE-MRI Sequences: Application to Breast Cancer Chemotherapy Response
Xia Zhu, Dipanjan Sengupta, Andrew Beers +2
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a minimally invasive imaging technique which can be used for characterizing tumor biology and tumor response to ra…
Segmenting Brain Tumors with Symmetry
Hejia Zhang, Xia Zhu, Theodore L. Willke
We explore encoding brain symmetry into a neural network for a brain tumor segmentation task. A healthy human brain is symmetric at a high level of abstraction, and the high-level…
A Searchlight Factor Model Approach for Locating Shared Information in Multi-Subject fMRI Analysis
Hejia Zhang, Po-Hsuan Chen, Janice Chen +5
There is a growing interest in joint multi-subject fMRI analysis. The challenge of such analysis comes from inherent anatomical and functional variability across subjects. One appr…
A Convolutional Autoencoder for Multi-Subject fMRI Data Aggregation
Po-Hsuan Chen, Xia Zhu, Hejia Zhang +5
Finding the most effective way to aggregate multi-subject fMRI data is a long-standing and challenging problem. It is of increasing interest in contemporary fMRI studies of human c…