Search Results for author:
Found 60 papers, 44 papers with code
Robust Learning via Conditional Prevalence Adjustment
CoPA assumes that (1) generation mechanism is stable, i. e. label Y and confounding variable(s) Z generate X, and (2) the unstable conditional prevalence in each site E fully accounts for the unstable correlations between X and Y .
Zero-shot Learning of Individualized Task Contrast Prediction from Resting-state Functional Connectomes
Given sufficient pairs of resting-state and task-evoked fMRI scans from subjects, it is possible to train ML models to predict subject-specific task-evoked activity using resting-state functional MRI (rsfMRI) scans.
Characterizing the Features of Mitotic Figures Using a Conditional Diffusion Probabilistic Model
In this paper, our goal is to shed light on the inherent uncertainty of mitosis labels and characterize the mitotic figure classification task in a human interpretable manner.
A Framework for Interpretability in Machine Learning for Medical Imaging
To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI.
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models.
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
By offering a more comprehensive approach to healthcare data integration, patchwork learning has the potential to revolutionize the clinical applicability of ML models.
A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints
Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression.
Modulating human brain responses via optimal natural image selection and synthetic image generation
Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images.
UniverSeg: Universal Medical Image Segmentation
We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training.
Learning to Compare Longitudinal Images
For example, normalizing for nuisance variation might be hard in settings where there are a lot of idiosyncratic changes.
Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing
Head MRI pre-processing involves converting raw images to an intensity-normalized, skull-stripped brain in a standard coordinate space.
Ranked #1 on
Reconstruction
on PPMI
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification
The first is the label weights, and the second is our novel concept of the ``support influence function,'' which is an easy-to-compute metric that quantifies the influence of a support element on the prediction for a given query.
LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping
In this paper, we present our new framework, called Learned Acquisition and Reconstruction Optimization (LARO), which aims to accelerate the multi-echo gradient echo (mGRE) pulse sequence for QSM.
GLACIAL: Granger and Learning-based Causality Analysis for Longitudinal Studies
We call our approach GLACIAL, which stands for "Granger and LeArning-based CausalIty Analysis for Longitudinal studies."
A Transformer-based Neural Language Model that Synthesizes Brain Activation Maps from Free-Form Text Queries
In this paper, we present Text2Brain, an easy to use tool for synthesizing brain activation maps from open-ended text queries.
Semi-Parametric Inducing Point Networks and Neural Processes
We introduce semi-parametric inducing point networks (SPIN), a general-purpose architecture that can query the training set at inference time in a compute-efficient manner.
Label conditioned segmentation
For segmentation tasks with multiple classes, the standard approach is to use a network that computes a multi-channel probabilistic segmentation map, with each channel representing one class.
Computing Multiple Image Reconstructions with a Single Hypernetwork
The typical approach is to train the model for a hyperparameter setting determined with some empirical or theoretical justification.
Hyper-Convolutions via Implicit Kernels for Medical Imaging
The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels.
Text2Brain: Synthesis of Brain Activation Maps from Free-form Text Query
In this work, we propose Text2Brain, a neural network approach for coordinate-based meta-analysis of neuroimaging studies to synthesize brain activation maps from open-ended text queries.
Ex uno plures: Splitting One Model into an Ensemble of Subnetworks
We show that the proposed subnetwork ensembling method can perform as well as standard deep ensembles in both accuracy and uncertainty estimates, yet with a computational efficiency similar to MC dropout.
Hyper-Convolution Networks for Biomedical Image Segmentation
In this paper, we propose a powerful novel building block, the hyper-convolution, which implicitly represents the convolution kernel as a function of kernel coordinates.
Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image.
NeuroGen: activation optimized image synthesis for discovery neuroscience
NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation.
Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks
In this paper, we explore a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic reconstruction model.
Predictive Modeling of Anatomy with Genetic and Clinical Data
We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory.
Neural encoding with visual attention
Using concurrent eye-tracking and functional Magnetic Resonance Imaging (fMRI) recordings from a large cohort of human subjects watching movies, we first demonstrate that leveraging gaze information, in the form of attentional masking, can significantly improve brain response prediction accuracy in a neural encoding model.
Intelligence plays dice: Stochasticity is essential for machine learning
Many fields view stochasticity as a way to gain computational efficiency, while often having to trade off accuracy.
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts fromResting-state Functional Connectivity
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals.
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation
We propose a simple, easy-to-optimize distillation method for learning the conditional predictive distribution of a pre-trained dropout model for fast, sample-free uncertainty estimation in computer vision tasks.
Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data
In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes.
A shared neural encoding model for the prediction of subject-specific fMRI response
The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models.
Self-Distillation as Instance-Specific Label Smoothing
It has been recently demonstrated that multi-generational self-distillation can improve generalization.
An Auto-Encoder Strategy for Adaptive Image Segmentation
Thus there is a strong need for deep learning-based segmentation tools that do not require heavy supervision and can continuously adapt.
Volumetric landmark detection with a multi-scale translation equivariant neural network
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
Volumetric landmark detection with a multi-scale shift equivariant neural network
Deep neural networks yield promising results in a wide range of computer vision applications, including landmark detection.
RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation
Brain lesion volume measured on T2 weighted MRI images is a clinically important disease marker in multiple sclerosis (MS).
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets
Motivated by this, we explore the use of various structured dropout techniques to promote model diversity and improve the quality of probabilistic predictions.
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that captures spontaneous brain activity patterns, revealing clues about the connectomic organization of the human brain.
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation
We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images.
Learning Conditional Deformable Templates with Convolutional Networks
We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks.
Deep-learning-based Optimization of the Under-sampling Pattern in MRI
In our experiments, we demonstrate that LOUPE-optimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods.
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
Motivated by this, we explore the use of structured dropout to promote model diversity and improve confidence calibration.
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).
Ranked #2 on
Diffeomorphic Medical Image Registration
on OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP
Constrained Diffeomorphic Image Registration
Deformable Medical Image Registration
+2
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable.
Learning-based Optimization of the Under-sampling Pattern in MRI
Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by under-sampling in k-space (i. e., the Fourier domain).
Machine learning in resting-state fMRI analysis
Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI.
A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures
We propose a novel machine learning strategy for studying neuroanatomical shape variation.
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.
Ranked #1 on
Diffeomorphic Medical Image Registration
on OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP
(Dice metric)
Deformable Medical Image Registration
Diffeomorphic Medical Image Registration
+1
Ensemble learning with 3D convolutional neural networks for connectome-based prediction
The specificty and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on pre-processing choices, such as the parcellation scheme used to define regions of interest (ROIs).
Medical Image Imputation from Image Collections
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing.
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and CCE.
Ranked #19 on
Learning with noisy labels
on CIFAR-10N-Worst
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm.
Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks
Mirroring the co-dependency of these tasks, our proposed model consists of two serial components: the first part computes a segmentation of cell bodies, while the second module identifies the centers of these cells.
A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome
An additional layer of complexity is that, in real life, the amount and type of data available for each patient can differ significantly.
An Unsupervised Learning Model for Deformable Medical Image Registration
We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
The health and function of tissue rely on its vasculature network to provide reliable blood perfusion.
