22 citations · 49 across the 6 of their papers we have counts for
6 papers
Deep-learning-enabled Brain Hemodynamic Mapping Using Resting-state fMRI
Xirui Hou, Pengfei Guo, Puyang Wang +18
Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging met…
Cross-Domain Federated Learning in Medical Imaging
Vishwa S Parekh, Shuhao Lai, Vladimir Braverman +4
Federated learning is increasingly being explored in the field of medical imaging to train deep learning models on large scale datasets distributed across different data centers wh…
A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity
Naresh Nandakumar, Niharika Shimona D'souza, Komal Manzoor +4
We present a novel deep learning framework that uses dynamic functional connectivity to simultaneously localize the language and motor areas of the eloquent cortex in brain tumor p…
Multiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
Vishwa S. Parekh, John Laterra, Chetan Bettegowda +3
Radiomics is an exciting new area of texture research for extracting quantitative and morphological characteristics of pathological tissue. However, to date, only single images hav…
Tumor Connectomics: Mapping the intra-tumoral complex interaction network
Vishwa S. Parekh, Michael A. Jacobs
Tumors are extremely heterogeneous and comprise of a number of intratumor microenvironments or sub-regions. These tumor microenvironments may interact with eac based on complex hig…
Stability and Localization of inter-individual differences in functional connectivity
Raag D. Airan, Joshua T. Vogelstein, Jay J. Pillai +3
Much recent attention has been paid to quantifying anatomic and functional neuroimaging on the individual subject level. For optimal individual subject characterization, specific a…