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Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation. (arXiv:2209.09563v1 [cs.LG])
cs.CV updates on arXiv.org arxiv.org
Uncertainty quantification in automated image analysis is highly desired in
many applications. Typically, machine learning models in classification or
segmentation are only developed to provide binary answers; however, quantifying
the uncertainty of the models can play a critical role for example in active
learning or machine human interaction. Uncertainty quantification is especially
difficult when using deep learning-based models, which are the state-of-the-art
in many imaging applications. The current uncertainty quantification approaches
do not scale well in high-dimensional real-world problems. Scalable …
arxiv deep learning medical quantification scalable segmentation uncertainty