most citedA Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

14 citations · 14 across the 1 of their papers we have counts for

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cs.CV2021★ 14 cited

A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation

Robin Camarasa, Daniel Bos, Jeroen Hendrikse +4

Uncertainty assessment has gained rapid interest in medical image analysis. A popular technique to compute epistemic uncertainty is the Monte-Carlo (MC) dropout technique. From a n…