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Found 7 papers, 2 papers with code
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests
Deep learning models are being adopted and applied on various critical decision-making tasks, yet they are trained to provide point predictions without providing degrees of confidence.
A Matrix Ensemble Kalman Filter-based Multi-arm Neural Network to Adequately Approximate Deep Neural Networks
Deep Learners (DLs) are the state-of-art predictive mechanism with applications in many fields requiring complex high dimensional data processing.
Spatio-temporal models of infectious disease with high rates of asymptomatic transmission
Next we use likelihood-based parameter estimation to derive temporally varying disease transmission parameters on the state-wide level.
Investigation of REFINED CNN ensemble learning for anti-cancer drug sensitivity prediction
REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN (Convolutional Neural Network) based models have shown promising results in drug sensitivity prediction.
REFINED (REpresentation of Features as Images with NEighborhood Dependencies): A novel feature representation for Convolutional Neural Networks
We present a novel approach for representation of high dimensional feature vector in a compact image form, termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies), that is conducible for convolutional neural network based deep learning.
Tumor ablation due to inhomogeneous -- anisotropic diffusion in generic 3-dimensional topologies
We derive a full 3-dimensional (3-D) model of inhomogeneous -- anisotropic diffusion in a tumor region coupled to a binary population model.
Medical Physics Tissues and Organs
Modeling of drug diffusion in a solid tumor leading to tumor cell death
It can be assumed that a minimum concentration is required for a cell to die, effectively connecting diffusion with efficacy.
