Abstract
The introduction of Deep Neural Networks in high-level applications is significantly increasing. However, the understanding of such model decisions by humans is not straightforward and may limit their use for critical applications. In order to address this issue, recent research work has introduced explanation methods, typically for classification and captioning. Nevertheless, for some tasks, explainability methods need to be developed. This includes image segmentation that is an essential component for many high-level applications. In this paper, we propose a general workflow allowing for the adaptation of a state of the art explainability methods, especially SHAP, to image segmentation tasks. The approach allows for explanation of single pixels as well image areas. We show the relevance of the approach on a critical application such as oil slick pollution detection on the sea surface. We also show the applicability of the method on a more standard multimedia domain semantic segmentation task. The conducted experiments highlight the relevant features on which the models derive their local results and help identify general model behaviours.
This work was supported by TotalEnergies company and also relied on HPC resources from GENCI-IDRIS (Grant 2021-AD011011418R1).
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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
Amri, E., Benoit, A., Bolon, P., Migebielle, V., Conche, B., Oppenheim, G.: Offshore oil slicks detection from SAR images through the mask-RCNN deep learning model. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2020). https://doi.org/10.1109/IJCNN48605.2020.9206652
Amri, E., et al.: Automatic offshore oil slick detection based on deep learning using SAR data and contextual information. In: Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2021, vol. 11857, pp. 35–42. SPIE (2021)
Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020)
Birch, C.P., Oom, S.P., Beecham, J.A.: Rectangular and hexagonal grids used for observation, experiment and simulation in ecology. Ecol. Model. 206(3–4), 347–359 (2007)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017)
Huang, C.H., Wu, H.Y., Lin, Y.L.: HarDNet-MSEG: a simple encoder-decoder polyp segmentation neural network that achieves over 0.9 mean dice and 86 FPS. arXiv preprint arXiv:2101.07172 (2021)
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 11–19 (2017)
Li, J., et al.: Lane-deeplab: lane semantic segmentation in automatic driving scenarios for high-definition maps. Neurocomputing 465, 15–25 (2021)
Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable AI: a review of machine learning interpretability methods. Entropy 23(1), 18 (2021)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 4768–4777 (2017)
Montavon, G., Samek, W., Müller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1–15 (2018)
Petsiuk, V., Das, A., Saenko, K.: Rise: randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421 (2018)
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, pp. 1135–1144 (2016)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE ICCV, pp. 618–626 (2017)
Shapley, L.S.: A Value for N-person Games. Princeton University Press, Princeton (2016)
Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. In: International Conference on Machine Learning, pp. 3145–3153. PMLR (2017)
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net. arXiv preprint arXiv:1412.6806 (2014)
Vinogradova, K., Dibrov, A., Myers, G.: Towards interpretable semantic segmentation via gradient-weighted class activation mapping (student abstract). In: Proceedings of the AAAI Conference on AI, vol. 34, pp. 13943–13944 (2020)
Yang, X., et al.: Towards automatic semantic segmentation in volumetric ultrasound. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 711–719. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66182-7_81
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Dardouillet, P., Benoit, A., Amri, E., Bolon, P., Dubucq, D., Credoz, A. (2023). Explainability of Image Semantic Segmentation Through SHAP Values. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_19
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