Search Results for author:
Found 11 papers, 8 papers with code
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes
Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings.
Pipeline-Invariant Representation Learning for Neuroimaging
We next propose two pipeline-invariant representation learning methodologies, MPSL and PXL, to improve robustness in classification performance and to capture similar neural network representations.
Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data
We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout.
On self-supervised multi-modal representation learning: An application to Alzheimer's disease
In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls.
Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease
Sensory input from multiple sources is crucial for robust and coherent human perception.
Whole MILC: generalizing learned dynamics across tasks, datasets, and populations
In this paper we present a novel self supervised training schema which reinforces whole sequence mutual information local to context (whole MILC).
Transfer Learning of fMRI Dynamics
In this paper, we demonstrate a self-supervised pre-training method that enables us to pre-train directly on fMRI dynamics of healthy control subjects and transfer the learning to much smaller datasets of schizophrenia.
Prediction of Progression to Alzheimer's disease with Deep InfoMax
Arguably, unsupervised learning plays a crucial role in the majority of algorithms for processing brain imaging.
Learning deep representations by mutual information estimation and maximization
In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder.
Almost instant brain atlas segmentation for large-scale studies
Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than 50 regions.
End-to-end learning of brain tissue segmentation from imperfect labeling
Segmenting a structural magnetic resonance imaging (MRI) scan is an important pre-processing step for analytic procedures and subsequent inferences about longitudinal tissue changes.
