Archana Venkataraman

Orcid: 0000-0003-2653-5591

According to our database1, Archana Venkataraman authored at least 60 papers between 2008 and 2023.

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Bibliography

2023
DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity.
IEEE Trans. Biomed. Eng., 2023

A Diffeomorphic Flow-Based Variational Framework for Multi-Speaker Emotion Conversion.
IEEE ACM Trans. Audio Speech Lang. Process., 2023

DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

A Deep Learning Framework to Localize the Epileptogenic Zone from Dynamic Functional Connectivity Using a Combined Graph Convolutional and Transformer Network.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds.
Proceedings of the Information Processing in Medical Imaging, 2023

Fusion Approaches to Predict Post-stroke Aphasia Severity from Multimodal Neuroimaging Data.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.
Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023


2022
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition.
CoRR, 2022

Prospective Learning: Back to the Future.
CoRR, 2022

SZLoc: A Multi-resolution Architecture for Automated Epileptic Seizure Localization from Scalp EEG.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease.
Proceedings of the Tenth International Conference on Learning Representations, 2022

2021
A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space.
NeuroImage, 2021

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.
NeuroImage, 2021

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge.
Medical Image Anal., 2021

Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks.
Medical Image Anal., 2021

A Deep-Bayesian Framework for Adaptive Speech Duration Modification.
CoRR, 2021

A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes.
CoRR, 2021

Automated inter-patient seizure detection using multichannel Convolutional and Recurrent Neural Networks.
Biomed. Signal Process. Control., 2021

G-MIND: an end-to-end multimodal imaging-genetics framework for biomarker identification and disease classification.
Proceedings of the Medical Imaging 2021: Image Processing, Online, February 15-19, 2021, 2021

M-GCN: A Multimodal Graph Convolutional Network to Integrate Functional and Structural Connectomics Data to Predict Multidimensional Phenotypic Characterizations.
Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

A Multi-scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize the Eloquent Cortex in Brain Tumor Patients.
Proceedings of the Information Processing in Medical Imaging, 2021

Predicting Acute Kidney Injury via Interpretable Ensemble Learning and Attention Weighted Convoutional-Recurrent Neural Networks.
Proceedings of the 55th Annual Conference on Information Sciences and Systems, 2021

Cross-site Epileptic Seizure Detection Using Convolutional Neural Networks.
Proceedings of the 55th Annual Conference on Information Sciences and Systems, 2021

2020
A Spatio-Temporal Model of Seizure Propagation in Focal Epilepsy.
IEEE Trans. Medical Imaging, 2020

A joint network optimization framework to predict clinical severity from resting state functional MRI data.
NeuroImage, 2020

Neuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning Challenge.
CoRR, 2020

A Multi-task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity.
Proceedings of the Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 2020

A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Non-Parallel Emotion Conversion Using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator.
Proceedings of the Interspeech 2020, 2020

Multi-Speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network.
Proceedings of the Interspeech 2020, 2020

2019
A generative-predictive framework to capture altered brain activity in fMRI and its association with genetic risk: application to Schizophrenia.
Proceedings of the Medical Imaging 2019: Image Processing, 2019

A Novel Graph Neural Network to Localize Eloquent Cortex in Brain Tumor Patients from Resting-State fMRI Connectivity.
Proceedings of the Connectomics in NeuroImaging - Third International Workshop, 2019

Bridging Imaging, Genetics, and Diagnosis in a Coupled Low-Dimensional Framework.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

Automated Noninvasive Seizure Detection and Localization Using Switching Markov Models and Convolutional Neural Networks.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019

A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces.
Proceedings of the Information Processing in Medical Imaging, 2019

Integrating Convolutional Neural Networks and Probabilistic Graphical Modeling for Epileptic Seizure Detection in Multichannel EEG.
Proceedings of the Information Processing in Medical Imaging, 2019

Weakly Supervised Syllable Segmentation by Vowel-Consonant Peak Classification.
Proceedings of the Interspeech 2019, 2019

A Multi-Speaker Emotion Morphing Model Using Highway Networks and Maximum Likelihood Objective.
Proceedings of the Interspeech 2019, 2019

Automated Emotion Morphing in Speech Based on Diffeomorphic Curve Registration and Highway Networks.
Proceedings of the Interspeech 2019, 2019

VESUS: A Crowd-Annotated Database to Study Emotion Production and Perception in Spoken English.
Proceedings of the Interspeech 2019, 2019

2018
Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging.
CoRR, 2018

Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields.
Proceedings of the Connectomics in NeuroImaging - Second International Workshop, 2018

A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2018, 2018

2017
A Unified Bayesian Approach to Extract Network-Based Functional Differences from a Heterogeneous Patient Cohort.
Proceedings of the Connectomics in NeuroImaging - First International Workshop, 2017

2016
Bayesian Community Detection in the Space of Group-Level Functional Differences.
IEEE Trans. Medical Imaging, 2016

2013
From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder.
IEEE Trans. Medical Imaging, 2013

Detecting Epileptic Regions Based on Global Brain Connectivity Patterns.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2013, 2013

2012
Generative models of brain connectivity for population studies.
PhD thesis, 2012

Joint Modeling of Anatomical and Functional Connectivity for Population Studies.
IEEE Trans. Medical Imaging, 2012

From Brain Connectivity Models to Identifying Foci of a Neurological Disorder.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, 2012

2010
Joint Generative Model for fMRI/DWI and Its Application to Population Studies.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2010

Robust feature selection in resting-state fMRI connectivity based on population studies.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010

2009
Exploring functional connectivity in fMRI via clustering.
Proceedings of the IEEE International Conference on Acoustics, 2009

2008
Signal approximation using the bilinear transform.
Proceedings of the IEEE International Conference on Acoustics, 2008

Spatial patterns and functional profiles for discovering structure in fMRI data.
Proceedings of the 42nd Asilomar Conference on Signals, Systems and Computers, 2008


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