Search Results for author:
Found 33 papers, 7 papers with code
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.
Learning Apparent Diffusion Coefficient Maps from Undersampled Radial k-Space Diffusion-Weighted MRI in Mice using a Deep CNN-Transformer Model in Conjunction with a Monoexponential Model
Purpose: To accelerate radially sampled diffusion weighted spin-echo (Rad-DW-SE) acquisition method for generating high quality of apparent diffusion coefficient (ADC) maps.
Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics
We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles.
Disentangling Alzheimer's disease neurodegeneration from typical brain aging using machine learning
First, a subset of AD patients and CN adults were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus AD).
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
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.
Meta-Learning for Natural Language Understanding under Continual Learning Framework
Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks.
Medical Image Harmonization Using Deep Learning Based Canonical Mapping: Toward Robust and Generalizable Learning in Imaging
Conventional and deep learning-based methods have shown great potential in the medical imaging domain, as means for deriving diagnostic, prognostic, and predictive biomarkers, and by contributing to precision medicine.
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.
Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging
Deep learning in k-space has demonstrated great potential for image reconstruction from undersampled k-space data in fast magnetic resonance imaging (MRI).
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.
Context-endcoding for neural network based skull stripping in magnetic resonance imaging
Skull stripping is usually the first step for most brain analysisprocess in magnetic resonance images.
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.
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection
Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans.
Automated Pulmonary Nodule Detection And Classification
Computed Tomography (CT)
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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.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
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.
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.
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.
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.
Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation
The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation results obtained by the supervised random forests method.
Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on Ultrasound imaging data
In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children.
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.
A dynamic graph-cuts method with integrated multiple feature maps for segmenting kidneys in ultrasound images
The proposed method has been evaluated and compared with state of the art image segmentation methods based on clinical kidney US images of 85 subjects.
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices.
Direct l_(2,p)-Norm Learning for Feature Selection
In this paper, we propose a novel sparse learning based feature selection method that directly optimizes a large margin linear classification model sparsity with l_(2, p)-norm (0 < p < 1)subject to data-fitting constraints, rather than using the sparsity as a regularization term.
