2019/10/17 by Ludwig Schallner, Schallner, Ludwig, Rabold, Johannes +4
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Cell Image Analysis Techniques #Explainable Artificial Intelligence (XAI) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1910.07856
openalex publication_date 2019/10/17 · openalex created_date 2020/07/23 · openalex updated_date 2026/07/28
End-to-end learning with deep neural networks, such as convolutional neural\nnetworks (CNNs), has been demonstrated to be very successful for different\ntasks of image classification. To make decisions of black-box approaches\ntransparent, different solutions have been proposed. LIME is an approach to\nexplainable AI relying on segmenting images into superpixels based on the\nQuick-Shift algorithm. In this paper, we present an explorative study of how\ndifferent superpixel methods, namely Felzenszwalb, SLIC and Compact-Watershed,\nimpact the generated visual explanations. We compare the resulting relevance\nareas with the image parts marked by a human reference. Results show that image\nparts selected as relevant strongly vary depending on the applied method.\nQuick-Shift resulted in the least and Compact-Watershed in the highest\ncorrespondence with the reference relevance areas.\n