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Found 21 papers, 11 papers with code
NeuroGraph: Benchmarks for Graph Machine Learning in Brain Connectomics
We delve deeply into the dataset generation search space by crafting 35 datasets within both static and dynamic contexts, running in excess of 15 baseline methods for benchmarking.
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI
Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
Image Classification with Consistent Supporting Evidence
We define consistent evidence to be both compatible and sufficient with respect to model predictions.
Equivariant Filters for Efficient Tracking in 3D Imaging
The transformation is then derived in closed form from the outputs of the filters.
Multimodal Representation Learning via Maximization of Local Mutual Information
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
Harmonization and the Worst Scanner Syndrome
We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable.
Bayesian Image Reconstruction using Deep Generative Models
Our method, Bayesian Reconstruction through Generative Models (BRGM), uses a single pre-trained generator model to solve different image restoration tasks, i. e., super-resolution and in-painting, by combining it with different forward corruption models.
Ranked #1 on
Image Inpainting
on FFHQ 1024 x 1024
DEMI: Discriminative Estimator of Mutual Information
Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data.
Overview of Scanner Invariant Representations
Pooled imaging data from multiple sources is subject to bias from each source.
Discovery and Separation of Features for Invariant Representation Learning
Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization.
Invariant Representations through Adversarial Forgetting
We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism.
Efficient Covariance Estimation from Temporal Data
Estimating the covariance structure of multivariate time series is a fundamental problem with a wide-range of real-world applications -- from financial modeling to fMRI analysis.
Exact Rate-Distortion in Autoencoders via Echo Noise
The noise is constructed in a data-driven fashion that does not require restrictive distributional assumptions.
Scanner Invariant Representations for Diffusion MRI Harmonization
Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation.
Connectivity-Driven Brain Parcellation via Consensus Clustering
We present two related methods for deriving connectivity-based brain atlases from individual connectomes.
Measures of Tractography Convergence
In the present work, we use information theory to understand the empirical convergence rate of tractography, a widely-used approach to reconstruct anatomical fiber pathways in the living brain.
Invariant Representations without Adversarial Training
Representations of data that are invariant to changes in specified factors are useful for a wide range of problems: removing potential biases in prediction problems, controlling the effects of covariates, and disentangling meaningful factors of variation.
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
There is no consensus on how to construct structural brain networks from diffusion MRI.
Fast structure learning with modular regularization
We also use our approach for estimating covariance structure for a number of real-world datasets and show that it consistently outperforms state-of-the-art estimators at a fraction of the computational cost.
A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i. e. parcellation.
An Empirical Study of Continuous Connectivity Degree Sequence Equivalents
In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes.
