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Towards Interpretability of Segmentation Networks by analyzing DeepDreams

Abstract : Interpretability of a neural network can be expressed as the identification of patterns or features to which the network can be either sensitive or indifferent. To this aim, a method inspired by DeepDream is proposed, where the activation of a neuron is maximized by performing gradient ascent on an input image. The method outputs curves that show the evolution of features during the maximization. A controlled experiment show how it enables assess the robustness to a given feature, or by contrast its sensitivity. The method is illustrated on the task of segmenting tumors in liver CT images.
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Submitted on : Friday, September 13, 2019 - 5:39:19 PM
Last modification on : Wednesday, November 3, 2021 - 6:18:19 AM


  • HAL Id : hal-02288076, version 1


Vincent Couteaux, O. Nempont, Guillaume Pizaine, Isabelle Bloch. Towards Interpretability of Segmentation Networks by analyzing DeepDreams. iMIMIC Workshop at MICCAI 2019: Interpretability of Machine Intelligence in Medical Image Computing, 2019, Shenzhen, China. pp.56-63. ⟨hal-02288076⟩



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