Niharika Shimona D'Souza

A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data

Mary Beth Nebel, Nicholas F. Wymbs, Archana Venkataraman, 2018, MICCAI.

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations pdf

Mary Beth Nebel, Archana Venkataraman, M. B. Nebel, 2020, NeuroImage.

A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces pdf

Mary Beth Nebel, Nicholas F. Wymbs, Archana Venkataraman, 2019, IPMI.

Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data pdf

Mary Beth Nebel, Nicholas F. Wymbs, Archana Venkataraman, 2019, MICCAI.

A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism pdf

Mary Beth Nebel, Archana Venkataraman, Stewart Mostofsky, 2020, MICCAI.

Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields

Jeff Craley, Archana Venkataraman, Haris I. Sair, 2018, CNI@MICCAI.

Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convoutional-Recurrent Neural Networks

Archana Venkataraman, Niharika Shimona D'Souza, Yu-Chung Peng, 2021, 2021 55th Annual Conference on Information Sciences and Systems (CISS).

M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations

Mary Beth Nebel, Archana Venkataraman, M. B. Nebel, 2021, MIDL.

A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes pdf

Mary Beth Nebel, Archana Venkataraman, M. B. Nebel, 2021, MICCAI.