When artificial intelligence is used in the medical sector, interpretability is a crucial factor to consider. Diagnosis based on machine decision produced by a black-box neural network, sometimes lacking clear rationale, is unlikely to be clinically adopted for fear of potentially dire consequences arising from unexplained misdiagnosis. In this work, we implement Layer-wise Relevance Propagation (LRP) , a visual interpretability method applied on 3D U-Net for lesion segmentation using the small dataset of multi-modal images provided by ISLES 2017 competition. We demonstrate that LRP modifications could provide more sensible visual explanations to an otherwise highly noise-skewed output and quantify them using inclusivity coefficients . We also link amplitude of modified signals to useful information content. High amplitude  signals appear to constitute the noise that undermines the interpretability capacity of LRP. Furthermore, mathematical framework for possible analysis of function approximation is developed by analogy.
We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal images provided by ISLES 2017 competition. We demonstrate that LRP modifications could provide more sensible visual explanations to an otherwise highly noise-skewed saliency map . We also link amplitude of modified signals to useful information content. High amplitude localized signals appear to constitute the noise that undermines the interpretability capacity of LRP. Furthermore, mathematical framework for possible analysis of function approximation is developed by analogy.
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When artificial intelligence is used in the medical sector, interpretability is a crucial factor to consider. Diagnosis based on machine decision produced by a black-box neural network, sometimes lacking clear rationale, is unlikely to be clinically adopted for fear of potentially dire consequences arising from unexplained misdiagnosis. In this work, we implement
We implement a visual interpretability method
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, a visual interpretability method applied on
on top of
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for lesion segmentation using
trained to perform lesion segmentation on
R
output and quantify them using inclusivity coefficients
saliency map
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localized