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Found 24 papers, 15 papers with code
Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation
We are able to enforce conditional independence of the diffusion autoencoder latent representation with respect to any protected attribute under the equalized odds constraint and show that this approach enables causal image generation with controllable latent spaces.
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.
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding
In this work, we propose a generalized notion of multi-task learning by incorporating both auxiliary tasks that the model should perform well on and adversarial tasks that the model should not perform well on.
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).
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.
Self-Supervised Longitudinal Neighbourhood Embedding
Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases.
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.
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.
