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Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade.
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Ribeiro, M.T., Singh, S., Guestrin, C.: "why should I trust you?": Explaining the predictions of any classifier. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C.C., Shen, D., Rastogi, R. (eds.) Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. pp. 1135–1144. ACM (2016). \href
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Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1
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Plumb, G., Molitor, D., Talwalkar, A.S.: Model agnostic supervised local explanations. In: Bengio, S., Wallach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada. pp. 2520–2529 (2018), https://proceedings.neurips.cc/paper/2018/hash/b495ce63ede0f4efc9eec62cb947c162-Abstract.html
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Schramowski, P., Stammer, W., Teso, S., Brugger, A., Herbert, F., Shao, X., Luigs, H., Mahlein, A., Kersting, K.: Making deep neural networks right for the right scientific reasons by interacting with their explanations. Nat. Mach. Intell. 2
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Yang, J., Shi, R., Ni, B.: MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI). pp. 191–195 (2021)
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