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Found 11 papers, 5 papers with code
A Comprehensive Study of Modern Architectures and Regularization Approaches on CheXpert5000
Computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets.
Recalibration of Aleatoric and Epistemic Regression Uncertainty in Medical Imaging
We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks and show that predictive uncertainty is systematically underestimated.
Uncertainty Calibration Error: A New Metric for Multi-Class Classification
Various metrics have recently been proposed to measure uncertainty calibration of deep models for classification.
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer
Using flow estimations from teacher model FlowNet2 we specialize a fast student model FlowNet2S on the patient-specific domain.
Calibration of Model Uncertainty for Dropout Variational Inference
The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration.
Well-Calibrated Regression Uncertainty in Medical Imaging with Deep Learning
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance.
Well-calibrated Model Uncertainty with Temperature Scaling for Dropout Variational Inference
Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration.
Deformable Medical Image Registration Using a Randomly-Initialized CNN as Regularization Prior
Our approach uses the idea of deep image priors to combine convolutional networks with conventional registration methods based on manually engineered priors.
Uncertainty Quantification in Computer-Aided Diagnosis: Make Your Model say "I don't know" for Ambiguous Cases
We evaluate two different methods for the integration of prediction uncertainty into diagnostic image classifiers to increase patient safety in deep learning.
Semantic denoising autoencoders for retinal optical coherence tomography
Noise in speckle-prone optical coherence tomography tends to obfuscate important details necessary for medical diagnosis.
Retinal OCT disease classification with variational autoencoder regularization
A recent study established a diagnostic tool based on convolutional neural networks (CNN), which was trained on a large database of retinal OCT images.
