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Found 12 papers, 12 papers with code
DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
Voxel-based multiple testing is widely used in neuroimaging data analysis.
Structure-consistent Restoration Network for Cataract Fundus Image Enhancement
In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure.
A comparative study of non-deep learning, deep learning, and ensemble learning methods for sunspot number prediction
An open-source Python package of our XGBoost-DL for the sunspot number prediction is available at https://github. com/yd1008/ts_ensemble_sunspot.
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation
Convolutional neural networks (CNNs) have achieved remarkable success in automatically segmenting organs or lesions on 3D medical images.
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning.
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images.
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
We propose a novel decomposition method for this model, called decomposition-based generalized canonical correlation analysis (D-GCCA).
CDPA: Common and Distinctive Pattern Analysis between High-dimensional Datasets
A representative model in integrative analysis of two high-dimensional correlated datasets is to decompose each data matrix into a low-rank common matrix generated by latent factors shared across datasets, a low-rank distinctive matrix corresponding to each dataset, and an additive noise matrix.
Sensitivity Analysis of Deep Neural Networks
Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations.
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.
Estimation of Large Covariance and Precision Matrices from Temporally Dependent Observations
In particular, the rates of convergence are obtained for the generalized thresholding estimation of covariance and correlation matrices, and for the constrained $\ell_1$ minimization and the $\ell_1$ penalized likelihood estimation of precision matrix.
Multiple Testing for Neuroimaging via Hidden Markov Random Field
Traditional voxel-level multiple testing procedures in neuroimaging, mostly $p$-value based, often ignore the spatial correlations among neighboring voxels and thus suffer from substantial loss of power.
