Search Results for author:
Found 26 papers, 15 papers with code
Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection
In this study, we introduce a novel component segmentation model for LA detection that leverages a few labeled samples and unlabeled images sharing logical constraints.
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs
Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity.
LSOR: Longitudinally-Consistent Self-Organized Representation Learning
Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs.
Imputing Brain Measurements Across Data Sets via Graph Neural Networks
An alternative is to impute the missing measurements via a deep learning approach.
An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment
One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems.
Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model
As acquiring MRIs is expensive, neuroscience studies struggle to attain a sufficient number of them for properly training deep learning models.
Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome
The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections.
Multiple Instance Neuroimage Transformer
As a proof-of-concept, we evaluate the efficacy of our model by training it to identify sex from T1w-MRIs of two public datasets: Adolescent Brain Cognitive Development (ABCD) and the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA).
Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing
A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i. e., whether certain factors (e. g., related to life events) are associated with an outcome (e. g., depression).
A Penalty Approach for Normalizing Feature Distributions to Build Confounder-Free Models
In this paper, we extend the MDN method by applying a Penalty approach (referred to as PDMN).
GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation
Because of this clinical data scarcity and inspired by the recent advances in self-supervised large-scale language models like GPT-3, we use human motion forecasting as an effective self-supervised pre-training task for the estimation of motor impairment severity.
Longitudinal Correlation Analysis for Decoding Multi-Modal Brain Development
Characterizing such complex brain development requires effective analysis of longitudinal and multi-modal neuroimaging data.
Metadata Normalization
Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods.
Representation Disentanglement for Multi-modal brain MR Analysis
To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities.
Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies.
Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity
This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity.
Longitudinal Self-Supervised Learning
Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative.
Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs
Many neurological diseases are characterized by gradual deterioration of brain structure and function.
Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis
The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain.
Representation Learning with Statistical Independence to Mitigate Bias
Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years.
Bias-Resilient Neural Network
We apply our method to a synthetic, a medical diagnosis, and a gender classification (Gender Shades) dataset.
Confounder-Aware Visualization of ConvNets
With recent advances in deep learning, neuroimaging studies increasingly rely on convolutional networks (ConvNets) to predict diagnosis based on MR images.
Variational AutoEncoder For Regression: Application to Brain Aging Analysis
While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored.
Truncated Gaussian-Mixture Variational AutoEncoder
In this paper we propose a novel generative process, in which we use a Gaussian-mixture to model a few major clusters in the data, and use a non-informative uniform distribution to capture the remaining data.
End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification
As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures.
Active Mean Fields for Probabilistic Image Segmentation: Connections with Chan-Vese and Rudin-Osher-Fatemi Models
A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary.
