Abstract
Nowadays, research in the field of artificial intelligence is focusing on the explainability of the developed algorithms, mainly neural networks. This trend is known as XAI and brings certain advantages such as increased confidence in the decision-making process, improved capacity for error analysis, verification of results and possibility of model refinement, among others. In this work we have focused on interpreting the predictions of recently developed deep learning models through different visualization techniques. The use case we introduce is the detection of breast cancer through the classification of mammographies, since the medical field is widely benefited by the contributions of XAI methods. Furthermore, the target neural networks are based on recent and poorly explored architectures: EfficientNet, designed to improve the performance of convolutional networks.
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Notes
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Instead of a division into three subsets, only two subsets are used, one of which is used both for validation during training and for testing.
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References
Dong, S., Wang, P., Abbas, K.: A survey on deep learning and its applications. Comput. Sci. Rev. 40, 100–379 (2021)
Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M.A., Kagal, L.: Explaining explanations: An overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89 (2018)
Singh, A., Sengupta, S., Lakshminarayanan, V.: Explainable deep learning models in medical image analysis. J. Imaging 6(6), 52 (2020)
Lenis, D., Major, D., Wimmer, M., Berg, A., Sluiter, G., Bühler, K.: Domain aware medical image classifier interpretation by counterfactual impact analysis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 315–325. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59710-8_31
Lamy, J.-B., Sekar, B., Guezennec, G., Bouaud, J., Séroussi, B.: Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach. Artif. Intell. Med. 94, 42–5 (2019)
Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)
Tan, M., Le, Q.V.: Efficientnet: rethinking model scaling for convolutional neural networks. ArXiv, abs/1905.11946 (2019)
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6–9 May 2019 (2019)
Liu, L., et al.: On the variance of the adaptive learning rate and beyond. In: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, 26–30 April 2020 (2020)
AL-Antari, M.A., Han, S.M., Kim, T.-S.: Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms. Comput. Meth. Prog. Biomed. 196, 105584 (2020)
Acknowledgements
The authors gratefully acknowledge research project PID2019- 110686RB-I00 of the State Research Program Oriented to the Challenges of Society.
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Rodriguez-Sampaio, M., Rincón, M., Valladares-Rodriguez, S., Bachiller-Mayoral, M. (2022). Explainable Artificial Intelligence to Detect Breast Cancer: A Qualitative Case-Based Visual Interpretability Approach. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_55
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