most citedA Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

q-bio.QM2024

Synthesizing study-specific controls using generative models on open access datasets for harmonized multi-study analyses

Shruti P. Gadewar, Alyssa H. Zhu, Iyad Ba Gari +5

Neuroimaging consortia can enhance reliability and generalizability of findings by pooling data across studies to achieve larger sample sizes. To adjust for site and MRI protocol e…

eess.IV2023

Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis

Nikhil J. Dhinagar, Amit Singh, Saket Ozarkar +11

Transfer learning represents a recent paradigm shift in the way we build artificial intelligence (AI) systems. In contrast to training task-specific models, transfer learning invol…

cs.CL20231 cited

Linking Symptom Inventories using Semantic Textual Similarity

Eamonn Kennedy, Shashank Vadlamani, Hannah M Lindsey +79

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, resul…

q-bio.QM20231 cited

A Comprehensive Corpus Callosum Segmentation Tool for Detecting Callosal Abnormalities and Genetic Associations from Multi Contrast MRIs

Shruti P. Gadewar, Elnaz Nourollahimoghadam, Ravi R. Bhatt +7

Structural alterations of the midsagittal corpus callosum (midCC) have been associated with a wide range of brain disorders. The midCC is visible on most MRI contrasts and in many…

eess.IV20231 cited

Efficiently Training Vision Transformers on Structural MRI Scans for Alzheimer's Disease Detection

Nikhil J. Dhinagar, Sophia I. Thomopoulos, Emily Laltoo +1

Neuroimaging of large populations is valuable to identify factors that promote or resist brain disease, and to assist diagnosis, subtyping, and prognosis. Data-driven models such a…