Abstract
Many techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications. It is an R package that can be used with the most usual image classification models. The paper shows results for typical benchmark images, as well as for a medical data set of gastro-enterological images. For comparison, results produced by the LIME method are included. Results show that CIU produces similar or better results than LIME with significantly shorter calculation times. However, the main purpose of this paper is to bring the existence of this package to general knowledge and use, rather than comparing with other explanation methods.
The work is partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.
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References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012). https://doi.org/10.1109/TPAMI.2012.120
Coelho, P., Pereira, A., Leite, A., Salgado, M., Cunha, A.: A deep learning approach for red lesions detection in video capsule endoscopies. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 553–561. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_63
Främling, K.: Modélisation et apprentissage des préférences par réseaux de neurones pour l’aide à la décision multicritère. Ph.D. thesis, INSA de Lyon, March 1996. https://tel.archives-ouvertes.fr/tel-00825854
Främling, K.: Contextual importance and utility in R: the ‘ciu’ package. In: Proceedings of 1st Workshop on Explainable Agency in Artificial Intelligence, at 35th AAAI Conference on Artificial Intelligence, pp. 110–114 (2021)
Malhi, A.K., Kampik, T., Pannu, H.S., Madhikermi, M., Främling, K.: Explaining machine learning-based classifications of in-vivo gastral images. In: 2019 Digital Image Computing: Techniques and Applications, DICTA 2019, Perth, Australia, 2–4 December 2019, pp. 1–7. IEEE (2019)
Pedersen, T.L., Benesty, M.: LIME: Local Interpretable Model-Agnostic Explanations (2019). R package version 0.5.1. https://CRAN.R-project.org/package=lime
Ribeiro, M.T., Singh, S., Guestrin, C.: “Why should i trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016, pp. 1135–1144 (2016)
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Appendix 1: Source Code for ImageNet Cat Results
Appendix 1: Source Code for ImageNet Cat Results
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Främling, K., Knapic̆, S., Malhi, A. (2021). ciu.image: An R Package for Explaining Image Classification with Contextual Importance and Utility. In: Calvaresi, D., Najjar, A., Winikoff, M., Främling, K. (eds) Explainable and Transparent AI and Multi-Agent Systems. EXTRAAMAS 2021. Lecture Notes in Computer Science(), vol 12688. Springer, Cham. https://doi.org/10.1007/978-3-030-82017-6_4
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