Search Results for author:
Found 24 papers, 4 papers with code
BUNDL: Bayesian Uncertainty-aware Deep Learning with Noisy training Labels for Seizure Detection in EEG
In this paper, we introduce a novel statistical framework that informs a deep learning model of label ambiguity, thereby enhancing the overall seizure detection performance.
QID$^2$: An Image-Conditioned Diffusion Model for Q-space Up-sampling of DWI Data
Our model, which we call QID$^2$, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction.
A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia
Taken together, the results of this study highlight the potential of LEGNet in effectively learning the relationships between rs-fMRI connectivity and language ability in a patient cohort with brain lesions for improved post-stroke aphasia evaluation.
Re-ENACT: Reinforcement Learning for Emotional Speech Generation using Actor-Critic Strategy
In this paper, we propose the first method to modify the prosodic features of a given speech signal using actor-critic reinforcement learning strategy.
mSPD-NN: A Geometrically Aware Neural Framework for Biomarker Discovery from Functional Connectomics Manifolds
Connectomics has emerged as a powerful tool in neuroimaging and has spurred recent advancements in statistical and machine learning methods for connectivity data.
A Diffeomorphic Flow-based Variational Framework for Multi-speaker Emotion Conversion
We term this new architecture a variational CycleGAN (VCGAN).
A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition
In parallel to these models, researchers have proposed several data augmentation techniques to increase the size and variability of existing labeled datasets.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Prospective Learning: Principled Extrapolation to the Future
We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Imaging Phenotypes of Disease
In parallel, our imaging network projects multimodal data onto a set of latent embeddings.
Adaptive Speech Duration Modification using a Deep-Generative Framework
We propose the first method to adaptively modify the duration of a given speechsignal.
A Deep-Bayesian Framework for Adaptive Speech Duration Modification
During inference, we generate the attention map as a proxy for the similarity matrix between the given input speech and an unknown target speech signal.
A Matrix Autoencoder Framework to Align the Functional and Structural Connectivity Manifolds as Guided by Behavioral Phenotypes
We propose a novel matrix autoencoder to map functional connectomes from resting state fMRI (rs-fMRI) to structural connectomes from Diffusion Tensor Imaging (DTI), as guided by subject-level phenotypic measures.
G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers.
A Multi-Task Deep Learning Framework to Localize the Eloquent Cortex in Brain Tumor Patients Using Dynamic Functional Connectivity
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 patients.
A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data
Our model consists of two coupled terms.
Deep sr-DDL: Deep Structurally Regularized Dynamic Dictionary Learning to Integrate Multimodal and Dynamic Functional Connectomics data for Multidimensional Clinical Characterizations
The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings.
Multi-speaker Emotion Conversion via Latent Variable Regularization and a Chained Encoder-Decoder-Predictor Network
Finally, the predictor uses the original spectrum and the modified F0 contour to generate a corresponding target spectrum.
Non-parallel Emotion Conversion using a Deep-Generative Hybrid Network and an Adversarial Pair Discriminator
We introduce a novel method for emotion conversion in speech that does not require parallel training data.
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data
The dictionary learning objective decomposes patient correlation matrices into a collection of shared basis networks and subject-specific loadings.
A Coupled Manifold Optimization Framework to Jointly Model the Functional Connectomics and Behavioral Data Spaces
We propose a coupled manifold optimization framework which projects fMRI data onto a low dimensional matrix manifold common to the cohort.
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism
The generative part of our framework is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying patient-specific loadings.
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge
A second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing.
Prediction of Autism Treatment Response from Baseline fMRI using Random Forests and Tree Bagging
We propose predicting patient response to PRT from baseline task-based fMRI by the novel application of a random forest and tree bagging strategy.
