Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning

@article{Wu2021QuantifyingPU,
  title={Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning},
  author={Zhiliang Wu and Yinchong Yang and Jindong Gu and Volker Tresp},
  journal={2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)},
  year={2021},
  pages={63-72}
}
Deep neural networks are increasingly being used for the analysis of medical images. However, most works neglect the uncertainty in the model’s prediction. We propose an uncertainty-aware deep kernel learning model which permits the estimation of the uncertainty in the prediction by a pipeline of a Convolutional Neural Network and a sparse Gaussian Process. Furthermore, we adapt different pre-training methods to investigate their impacts on the proposed model. We apply our approach to Bone Age… 

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