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We implement a visual interpretability method Layer-wise RelevancePropagation (LRP) on top of 3D U-Net trained to perform lesion segmentation onthe small dataset of multi-modal images provided by ISLES 2017 competition. Wedemonstrate that LRP modifications could provide more sensible visualexplanations to an otherwise highly noise-skewed saliency map. We also linkamplitude of modified signals to useful information content. High amplitudelocalized signals appear to constitute the noise that undermines theinterpretability capacity of LRP. Furthermore, mathematical framework forpossible analysis of function approximation is developed by analogy.

Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks