7 citations · 13 across the 12 of their papers we have counts for
13 papers
Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction
Yu Chang, Anzhe Cheng, Jiahao Chen +8
Brain-age prediction from resting-state fMRI provides a quantitative framework for characterizing age-related changes in spontaneous brain dynamics and for identifying functional s…
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
Yu Chang, Anzhe Cheng, Chenwei Wu +7
The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction. Recent multimodal models have adv…
Anchoring the Eigengap: Cross-Modal Spectral Stabilization for Sample-Efficient Representation Learning
Nikhil J. Dhinagar, Vidhi Chhatbar, Chirag Jagad +6
Deep vision models degrade sharply in low-data regimes, particularly in medical imaging where labeled samples are scarce. We show this arises not merely from overfitting but from a…
Multi-modal Imputation for Alzheimer's Disease Classification
Abhijith Shaji, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modaliti…
ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization
Anzhe Cheng, Shukai Duan, Shixuan Li +8
Mixture-of-Experts (MoE) architectures expand model capacity by sparsely activating experts but face two core challenges: misalignment between router logits and each expert's inter…
Diffusion Bridge Models for 3D Medical Image Translation
Shaorong Zhang, Tamoghna Chattopadhyay, Sophia I. Thomopoulos +3
Diffusion tensor imaging (DTI) provides crucial insights into the microstructure of the human brain, but it can be time-consuming to acquire compared to more readily available T1-w…
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…
Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model
Roberto Goya-Maldonado, Tracy Erwin-Grabner, Ling-Li Zeng +84
Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if…
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…
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…
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…
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…
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
Vladimir Belov, Tracy Erwin-Grabner, Ali Saffet Gonul +63
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predict…