Fetching the paper…
Reading the bibliography…
The wide-spread adoption of representation learning technologies in clinical decision making strongly emphasizes the need for characterizing model reliability and enabling rigorous introspection of model behavior.
Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artificial Intelligence in medicine 23
2001
Earlier work this paper cites.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13
2004
Earlier work this paper cites.
Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521
2015
Earlier work this paper cites.
Kuleshov, V., Liang, P.S.: Calibrated structured prediction. In: Advances in Neural Information Processing Systems. pp. 3474–3482 (2015)
2015
Earlier work this paper cites.
Gal, Y.: Uncertainty in deep learning. University of Cambridge 1
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Ribeiro, M., Singh, S., Guestrin, C.: “Why should I trust you?” Explaining the predictions of any classifier. In: ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining (2016)
2016
Earlier work this paper cites.
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. pp. 1321–1330. JMLR. org (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Leibig, C., Allken, V., Ayhan, M.S., Berens, P., Wahl, S.: Leveraging uncertainty information from deep neural networks for disease detection. Scientific reports 7
2017
Earlier work this paper cites.
Lundberg, S., Lee, S.I.: Unified framework for interpretable methods. In: Advances of Neural Inf. Proc. Systems (2017)
2017
Cited alongside, same era.
Schulam, P., Saria, S.: Reliable decision support using counterfactual models. In: Advances in Neural Information Processing Systems. pp. 1697–1708 (2017)
2017
Cited alongside, same era.
Ahmad, M.A., Eckert, C., Teredesai, A.: Interpretable machine learning in healthcare. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. pp. 559–560 (2018)
2018
Cited alongside, same era.
Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.M., Zietz, M., Hoffman, M.M., et al.: Opportunities and obstacles for deep learning in biology and medicine. Journal of The Royal Society Interface 15
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
Seo, S., Seo, P.H., Han, B.: Learning for single-shot confidence calibration in deep neural networks through stochastic inferences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9030–9038 (2019)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Faust, O., Hagiwara, Y., Hong, T.J., Lih, O.S., Acharya, U.R.: Deep learning for healthcare applications based on physiological signals: A review. Computer methods and programs in biomedicine 161
2018
Cited alongside, same era.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM computing surveys (CSUR) 51
2018
Cited alongside, same era.
Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Briefings in bioinformatics 19
2018
Cited alongside, same era.
Montavon, G., Samek, W., Müller, K.R.: Methods for interpreting and understanding deep neural networks. Digital Signal Processing 73
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5
2018
Cited alongside, same era.
2019
Later among the works it cites.
2019
Later among the works it cites.
Thiagarajan, J.J., Venkatesh, B., Sattigeri, P., Bremer, P.T.: Building calibrated deep models via uncertainty matching with auxiliary interval predictors. AAAI Conference on Artificial Intelligence (2019)
2019
Later among the works it cites.
Thulasidasan, S., Chennupati, G., Bilmes, J.A., Bhattacharya, T., Michalak, S.: On mixup training: Improved calibration and predictive uncertainty for deep neural networks. In: Advances in Neural Information Processing Systems. pp. 13888–13899 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.