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Found 12 papers, 1 papers with code
Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts.
Deep Learning-Based Assessment of Cerebral Microbleeds in COVID-19
Cerebral Microbleeds (CMBs), typically captured as hypointensities from susceptibility-weighted imaging (SWI), are particularly important for the study of dementia, cerebrovascular disease, and normal aging.
FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain
A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions.
Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networks
This study focuses on hybrid deep neural networks to predict traffic flow in the California Freeway Performance Measurement System (PeMS) with missing values.
CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data
Learning temporal patterns from multivariate longitudinal data is challenging especially in cases when data is sporadic, as often seen in, e. g., healthcare applications where the data can suffer from irregularity and asynchronicity as the time between consecutive data points can vary across features and samples, hindering the application of existing deep learning models that are constructed for complete, evenly spaced data with fixed sequence lengths.
On the Initialization of Long Short-Term Memory Networks
Weight initialization is important for faster convergence and stability of deep neural networks training.
Robust parametric modeling of Alzheimer's disease progression
Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric MRI and PET biomarkers, CSF measurements, as well as cognitive tests.
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i. e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method.
Robust training of recurrent neural networks to handle missing data for disease progression modeling
This paper shows that built-in handling of missing values in LSTM network training paves the way for application of RNNs in disease progression modeling.
Combining Multiple Views for Visual Speech Recognition
In this paper, we explore this aspect and provide a comprehensive study on combining multiple views for visual speech recognition.
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System
Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available.
A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition
Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets.
