Humer, ChristinaElharty, MohamedHinterreiter, AndreasStreit, MarcKrone, MichaelLenti, SimoneSchmidt, Johanna2022-06-022022-06-022022978-3-03868-185-4https://doi.org/10.2312/evp.20221130https://diglib.eg.org:443/handle/10.2312/evp20221130Explanations of deep neural networks (DNNs) give users a better understanding of the inner workings and generalizability of a network. While the majority of research focuses on explanations for classification networks, in this work we focus on explainability for image segmentation networks. As a first contribution, we introduce a lightweight framework that allows generalizing certain attribution-based explanations, originally developed for classification networks, to also work for segmentation networks. The second contribution is a web-based tool that utilizes this framework and allows users to interactively explore segmentation networks. We demonstrate the approach using a self-trained mushroom segmentation network.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing --> Visual analytics; Computing methodologies --> Image segmentationHuman centered computingVisual analyticsComputing methodologiesImage segmentation10.2312/evp.2022113099-1013 pages