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Abstract #2652

Sensitivity of a Deep Learning Model for Multi-Sequence Cardiac Pathology Segmentation to Input Data Transformations

Markus J Ankenbrand1, Liliya Shainberg1, Michael Hock1, David Lohr1, and Laura Maria Schreiber1
1Chair of Cellular and Molecular Imaging, Comprehensive Heart Failure Center (CHFC), University Hospital Würzburg, Würzburg, Germany

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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