• Corpus ID: 237571569

Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection

@article{Akrami2021DeepQR,
  title={Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection},
  author={Haleh Akrami and Anand A. Joshi and Serg{\"u}l Ayd{\"o}re and Richard Leahy},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.09374}
}
Despite impressive state-of-the-art performance on a wide variety of machine learning tasks in multiple applications, deep learning methods can produce over-confident predictions, particularly with limited training data. Therefore, quantifying uncertainty is particularly important in critical applications such as lesion detection and clinical diagnosis, where a realistic assessment of uncertainty is essential in determining surgical margins, disease status and appropriate treatment. In this work… 

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