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Found 12 papers, 2 papers with code
Cross-Vendor CT Image Data Harmonization Using CVH-CT
We propose a novel deep learning approach called CVH-CT for harmonizing CT images captured using scanners from different vendors.
CT Image Harmonization for Enhancing Radiomics Studies
While remarkable advances have been made in Computed Tomography (CT), capturing CT images with non-standardized protocols causes low reproducibility regarding radiomic features, forming a barrier on CT image analysis in a large scale.
STAN-CT: Standardizing CT Image using Generative Adversarial Network
Computed tomography (CT) plays an important role in lung malignancy diagnostics and therapy assessment and facilitating precision medicine delivery.
GPCA: A Probabilistic Framework for Gaussian Process Embedded Channel Attention
In this paper, we propose Gaussian process embedded channel attention (GPCA) module and further interpret the channel attention schemes in a probabilistic way.
Approximated Orthonormal Normalisation in Training Neural Networks
Considering a weight matrix W from a particular neural layer in the model, our objective is to design a function h(W) such that its row vectors are approximately orthogonal to each other while allowing the DNN model to fit the training data sufficiently accurate.
Automated Classification of Seizures against Nonseizures: A Deep Learning Approach
In order to capture essential seizure features, this paper integrates an emerging deep learning model, the independently recurrent neural network (IndRNN), with a dense structure and an attention mechanism to exploit temporal and spatial discriminating features and overcome seizure variabilities.
Combining Representation Learning with Tensor Factorization for Risk Factor Analysis - an application to Epilepsy and Alzheimer's disease
Existing studies consider Alzheimer's disease (AD) a comorbidity of epilepsy, but also recognize epilepsy to occur more frequently in patients with AD than those without.
A Novel Independent RNN Approach to Classification of Seizures against Non-seizures
In current clinical practices, electroencephalograms (EEG) are reviewed and analyzed by trained neurologists to provide supports for therapeutic decisions.
Rapidly Adapting Moment Estimation
Adaptive gradient methods such as Adam have been shown to be very effective for training deep neural networks (DNNs) by tracking the second moment of gradients to compute the individual learning rates.
Object Detection and 3D Estimation via an FMCW Radar Using a Fully Convolutional Network
This paper considers object detection and 3D estimation using an FMCW radar.
BALSON: Bayesian Least Squares Optimization with Nonnegative L1-Norm Constraint
In order to explicitly characterize the nonnegative L1-norm constraint of the parameters, we further approximate the true posterior distribution by a Dirichlet distribution.
Training Deep Neural Networks via Optimization Over Graphs
In this work, we propose to train a deep neural network by distributed optimization over a graph.
