Validating Uncertainty in Medical Image Translation

Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians. However, standard deep neural networks do not provide a reliable measure of uncertainty in those quantitative values. Recent work has shown that using dropout during training and testing can provide estimates of uncertainty. In this work, we investigate using dropout to estimate epistemic and aleatoric uncertainty in a CT-to-MR image translation task. We show that both types of uncertainty are captured, as defined, providing confidence in the output uncertainty estimates.

[1]  A. Kiureghian,et al.  Aleatory or epistemic? Does it matter? , 2009 .

[2]  Frank Hutter,et al.  Decoupled Weight Decay Regularization , 2017, ICLR.

[3]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[4]  Vincent Dumoulin,et al.  Deconvolution and Checkerboard Artifacts , 2016 .

[5]  Antonio Criminisi,et al.  Uncertainty Quantification in Deep Learning for Safer Neuroimage Enhancement , 2019, ArXiv.

[6]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[7]  Doina Precup,et al.  Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation , 2018, MICCAI.

[8]  Aaron Carass,et al.  Finding novelty with uncertainty , 2020, Medical Imaging: Image Processing.

[9]  M. Jorge Cardoso,et al.  Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning , 2018, MICCAI.

[10]  Aaron Carass,et al.  Whole Brain Segmentation and Labeling from CT Using Synthetic MR Images , 2017, MLMI@MICCAI.

[11]  Alex Kendall,et al.  What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.

[12]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[13]  Aaron Carass,et al.  Evaluating the Impact of Intensity Normalization on MR Image Synthesis , 2018, Image Processing.

[14]  Kilian Q. Weinberger,et al.  On Calibration of Modern Neural Networks , 2017, ICML.

[15]  Jonathan Tompson,et al.  Efficient object localization using Convolutional Networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).