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Despite U-Nets being the de-facto standard for medical image segmentation, researchers have identified shortcomings of U-Nets, such as overconfidence and poor out-of-distribution generalization. Several methods for uncertainty quantification try to solve such problems by relying on well-known approximations such as Monte-Carlo Drop-Out, Probabilistic U-Net, and Stochastic Segmentation Networks. We introduce a novel multi-headed Variational U-Net. The proposed approach combines the global exploration capabilities of deep ensembles with the out-of-distribution robustness of Variational Inference. An efficient training strategy and an expressive yet general design ensure superior uncertainty quantification within a reasonable compute requirement. We thoroughly analyze the performance and properties of our approach on the publicly available BRATS2018 dataset. Further, we test our model on four commonly observed distribution shifts. The proposed approach has good uncertainty calibration and is robust to out-of-distribution shifts.
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Wednesday 6th July
Poster Session 1.1 - onsite 15:20 - 16:20, virtual 11:00 - 12:00 (UTC+2)
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