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Found 22 papers, 5 papers with code
Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance.
SurfNN: Joint Reconstruction of Multiple Cortical Surfaces from Magnetic Resonance Images
Different from existing deep learning-based cortical surface reconstruction methods that either reconstruct the cortical surfaces separately or neglect the interdependence between the inner and outer surfaces, SurfNN reconstructs both the inner and outer cortical surfaces jointly by training a single network to predict a midthickness surface that lies at the center of the inner and outer cortical surfaces.
Deep Clustering Survival Machines with Interpretable Expert Distributions
We learn weights of the expert distributions for individual instances according to their features discriminatively such that each instance's survival information can be characterized by a weighted combination of the learned constant expert distributions.
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision Making
In this paper, we extend the classic CS divergence to quantify the closeness between two conditional distributions and show that the developed conditional CS divergence can be simply estimated by a kernel density estimator from given samples.
Causal Recurrent Variational Autoencoder for Medical Time Series Generation
We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporates the underlying causal mechanism into its data generation process.
Deep Deterministic Independent Component Analysis for Hyperspectral Unmixing
We develop a new neural network based independent component analysis (ICA) method by directly minimizing the dependence amongst all extracted components.
Unsupervised deep learning for individualized brain functional network identification
A novel unsupervised deep learning method is developed to identify individual-specific large scale brain functional networks (FNs) from resting-state fMRI (rsfMRI) in an end-to-end learning fashion.
MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision
We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting.
ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation
However, existing 2D deep learning methods are not equipped to effectively capture 3D spatial contextual information that is needed to achieve accurate brain structure segmentation.
Feature-Fused Context-Encoding Network for Neuroanatomy Segmentation
In this way, the network is guaranteed to be aware of the class-dependent feature maps to facilitate the segmentation.
A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI
Introduction: It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.
Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network
It remains challenging to automatically segment kidneys in clinical ultrasound images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer
To improve existing survival analysis techniques whose performance is hinged on imaging features, we propose a deep learning method to build survival regression models by optimizing imaging features with deep convolutional neural networks (CNNs) in a proportional hazards model.
Early Prediction of Alzheimer's Disease Dementia Based on Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders.
Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance.
Identification of multi-scale hierarchical brain functional networks using deep matrix factorization
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organization from resting state fMRI data.
Brain decoding from functional MRI using long short-term memory recurrent neural networks
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies.
Identification of temporal transition of functional states using recurrent neural networks from functional MRI
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes.
Non-Rigid Image Registration Using Self-Supervised Fully Convolutional Networks without Training Data
A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework.
Brain Age Prediction Based on Resting-State Functional Connectivity Patterns Using Convolutional Neural Networks
Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance.
Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data
Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data.
Non-rigid image registration using fully convolutional networks with deep self-supervision
We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered.
