Search Results for author:
Found 12 papers, 0 papers with code
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.
Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Inspired by recent work in semi-supervised learning we introduce a novel method to adapt from one source domain to $n$ target domains (as long as there is paired data covering all domains).
Knowledge distillation for semi-supervised domain adaptation
As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.
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.
PADDIT: Probabilistic Augmentation of Data using Diffeomorphic Image Transformation
For proper generalization performance of convolutional neural networks (CNNs) in medical image segmentation, the learnt features should be invariant under particular non-linear shape variations of the input.
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--.
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.
A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
Template estimation plays a crucial role in computational anatomy since it provides reference frames for performing statistical analysis of the underlying anatomical population variability.
Label Stability in Multiple Instance Learning
We address the problem of \emph{instance label stability} in multiple instance learning (MIL) classifiers.
Classification of COPD with Multiple Instance Learning
Chronic obstructive pulmonary disease (COPD) is a lung disease where early detection benefits the survival rate.
Transfer learning for multi-center classification of chronic obstructive pulmonary disease
We show that Gaussian texture features outperform intensity features previously used in multi-center classification tasks.
