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07/06/2023
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging
Most state-of-the-art techniques for medical image segmentation rely on ...
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05/10/2023
Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources
Machine learning (ML) in healthcare presents numerous opportunities for ...
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04/19/2023
A robust and interpretable deep learning framework for multi-modal registration via keypoints
We present KeyMorph, a deep learning-based image registration framework ...
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04/12/2023
UniverSeg: Universal Medical Image Segmentation
While deep learning models have become the predominant method for medica...
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04/05/2023
Learning to Compare Longitudinal Images
Longitudinal studies, where a series of images from the same set of indi...
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03/21/2023
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-n...
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12/07/2022
A Simple Nadaraya-Watson Head can offer Explainable and Calibrated Classification
In this paper, we empirically analyze a simple, non-learnable, and nonpa...
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11/01/2022
LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping
Quantitative susceptibility mapping (QSM) involves acquisition and recon...
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05/24/2022
Semi-Parametric Deep Neural Networks in Linear Time and Memory
Recent advances in deep learning have been driven by large-scale paramet...
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03/17/2022
Label conditioned segmentation
Semantic segmentation is an important task in computer vision that is of...
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02/22/2022
Computing Multiple Image Reconstructions with a Single Hypernetwork
Deep learning based techniques achieve state-of-the-art results in a wid...
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02/06/2022
Hyper-Convolutions via Implicit Kernels for Medical Imaging
The convolutional neural network (CNN) is one of the most commonly used ...
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06/09/2021
Ex uno plures: Splitting One Model into an Ensemble of Subnetworks
Monte Carlo (MC) dropout is a simple and efficient ensembling method tha...
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05/21/2021
Hyper-Convolution Networks for Biomedical Image Segmentation
The convolution operation is a central building block of neural network ...
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05/17/2021
Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whe...
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05/15/2021
NeuroGen: activation optimized image synthesis for discovery neuroscience
Functional MRI (fMRI) is a powerful technique that has allowed us to cha...
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01/06/2021
Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks
Reconstructing under-sampled k-space measurements in Compressed Sensing ...
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10/09/2020
Predictive Modeling of Anatomy with Genetic and Clinical Data
We present a semi-parametric generative model for predicting anatomy of ...
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10/01/2020
Neural encoding with visual attention
Visual perception is critically influenced by the focus of attention. Du...
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08/17/2020
Intelligence plays dice: Stochasticity is essential for machine learning
Many fields view stochasticity as a way to gain computational efficiency...
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08/07/2020
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...
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07/31/2020
Learning the Distribution: A Unified Distillation Paradigm for Fast Uncertainty Estimation in Computer Vision
Calibrated estimates of uncertainty are critical for many real-world com...
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07/29/2020
Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing unde...
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06/29/2020
A shared neural encoding model for the prediction of subject-specific fMRI response
The increasing popularity of naturalistic paradigms in fMRI (such as mov...
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06/09/2020
Self-Distillation as Instance-Specific Label Smoothing
It has been recently demonstrated that multi-generational self-distillat...
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04/29/2020
An Auto-Encoder Strategy for Adaptive Image Segmentation
Deep neural networks are powerful tools for biomedical image segmentatio...
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03/03/2020
Volumetric landmark detection with a multi-scale shift equivariant neural network
Deep neural networks yield promising results in a wide range of computer...
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02/27/2020
RSANet: Recurrent Slice-wise Attention Network for Multiple Sclerosis Lesion Segmentation
Brain lesion volume measured on T2 weighted MRI images is a clinically i...
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08/16/2019
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach
Resting-state functional MRI (rs-fMRI) is a rich imaging modality that c...
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08/13/2019
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation
We tackle biomedical image segmentation in the scenario of only a few la...
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08/07/2019
Learning Conditional Deformable Templates with Convolutional Networks
We develop a learning framework for building deformable templates, which...
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07/26/2019
Adaptive Compressed Sensing MRI with Unsupervised Learning
In compressed sensing MRI, k-space measurements are under-sampled to ach...
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06/23/2019
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
In classification applications, we often want probabilistic predictions ...
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04/25/2019
Unsupervised deep learning for Bayesian brain MRI segmentation
Probabilistic atlas priors have been commonly used to derive adaptive an...
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03/08/2019
Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces
Classical deformable registration techniques achieve impressive results ...
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03/08/2019
Unsupervised Data Imputation via Variational Inference of Deep Subspaces
A wide range of systems exhibit high dimensional incomplete data. Accura...
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03/07/2019
Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation
We consider the problem of segmenting a biomedical image into anatomical...
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01/07/2019
Learning-based Optimization of the Under-sampling Pattern in MRI
Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated...
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12/30/2018
Machine learning in resting-state fMRI analysis
Machine learning techniques have gained prominence for the analysis of r...
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10/17/2018
A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures
We propose a novel machine learning strategy for studying neuroanatomica...
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09/14/2018
VoxelMorph: A Learning Framework for Deformable Medical Image Registration
We present VoxelMorph, a fast, unsupervised, learning-based algorithm fo...
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09/11/2018
Ensemble learning with 3D convolutional neural networks for connectome-based prediction
The specificty and sensitivity of resting state functional MRI (rs-fMRI)...
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08/17/2018
Medical Image Imputation from Image Collections
We present an algorithm for creating high resolution anatomically plausi...
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05/20/2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Deep neural networks (DNNs) have achieved tremendous success in a variet...
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05/11/2018
Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration
Traditional deformable registration techniques achieve impressive result...
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05/08/2018
Joint Cell Nuclei Detection and Segmentation in Microscopy Images Using 3D Convolutional Networks
We propose a 3D convolutional neural network to simultaneously segment a...
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03/13/2018
A Probabilistic Disease Progression Model for Predicting Future Clinical Outcome
In this work, we consider the problem of predicting the course of a prog...
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02/07/2018
An Unsupervised Learning Model for Deformable Medical Image Registration
We present an efficient learning-based algorithm for deformable, pairwis...
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01/03/2018
