From Explanations to Segmentation: Using Explainable AI for Image Segmentation

• URL: http://arxiv.org/abs/2202.00315v1
• Date: Tue, 1 Feb 2022 10:26:10 GMT
• Title: From Explanations to Segmentation: Using Explainable AI for Image Segmentation
• Authors: Clemens Seibold, Johannes K\"unzel, Anna Hilsmann, Peter Eisert
• Abstract summary: We build upon the advances of the Explainable AI (XAI) community and extract a pixel-wise binary segmentation. We show that we achieve similar results compared to an established U-Net segmentation architecture. The proposed method can be trained in a weakly supervised fashion, as the training samples must be only labeled on image-level.
• Score: 1.8581514902689347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The new era of image segmentation leveraging the power of Deep Neural Nets (DNNs) comes with a price tag: to train a neural network for pixel-wise segmentation, a large amount of training samples has to be manually labeled on pixel-precision. In this work, we address this by following an indirect solution. We build upon the advances of the Explainable AI (XAI) community and extract a pixel-wise binary segmentation from the output of the Layer-wise Relevance Propagation (LRP) explaining the decision of a classification network. We show that we achieve similar results compared to an established U-Net segmentation architecture, while the generation of the training data is significantly simplified. The proposed method can be trained in a weakly supervised fashion, as the training samples must be only labeled on image-level, at the same time enabling the output of a segmentation mask. This makes it especially applicable to a wider range of real applications where tedious pixel-level labelling is often not possible.

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