Corpus ID: 22323188

Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation

  title={Uncertainty-driven Sanity Check: Application to Postoperative Brain Tumor Cavity Segmentation},
  author={Alain Jungo and R. Meier and Ekin Ermis and E. Herrmann and M. Reyes},
  • Alain Jungo, R. Meier, +2 authors M. Reyes
  • Published 2018
  • Computer Science
  • ArXiv
  • Uncertainty estimates of modern neuronal networks provide additional information next to the computed predictions and are thus expected to improve the understanding of the underlying model. Reliable uncertainties are particularly interesting for safety-critical computer-assisted applications in medicine, e.g., neurosurgical interventions and radiotherapy planning. We propose an uncertainty-driven sanity check for the identification of segmentation results that need particular expert review. Our… CONTINUE READING

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    Publications referenced by this paper.
    Adam: A Method for Stochastic Optimization
    • 49,946
    • PDF
    Densely Connected Convolutional Networks
    • 9,761
    • PDF
    What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
    • 1,066
    • PDF
    Bayesian learning for neural networks
    • 3,145
    • PDF
    Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
    • 890
    • PDF
    A Practical Bayesian Framework for Backpropagation Networks
    • 2,068
    • PDF
    The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation
    • 752
    • PDF
    Uncertainty in Deep Learning
    • 569
    • PDF
    Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
    • 331
    • PDF