Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

11 Feb 2017Vanya V. ValindriaIoannis LavdasWenjia BaiKonstantinos KamnitsasEric O. AboagyeAndrea G. RockallDaniel RueckertBen Glocker

When integrating computational tools such as automatic segmentation into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data, and in particular, to detect when an automatic method fails. However, this is difficult to achieve due to absence of ground truth... (read more)

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