Deep learning-based segmentation models play an important role in cardiac magnetic resonance imaging. While their performance is good on the training and validation data the models themselves are hard to interpret. Sensitivity analysis helps to estimate the effect of different data characteristics on segmentation performance. We demonstrate that a published model exhibits higher sensitivity to basic transformations like rotation for pathology classes than for tissue classes in general.
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