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From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIACM computing surveys, 55(13s), Article 295. Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., Van Keulen, M. & Seifert, C. Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings. Pathak, S., Schlötterer, J., Geerdink, J., Vijlbrief, O. D., Keulen, M. v. & Seifert, C.Benchmarking eXplainable AI: A Survey on Available Toolkits and Open ChallengesIn Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 (pp. 6665-6673). International Joint Conferences on Artificial Intelligence. Le, P. Q., Nauta, M., Nguyen, V. B., Pathak, S., Schlötterer, J. & Seifert, C.
STQS: Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoringArtificial intelligence in medicine, 114, Article 102038. Pathak, S., Lu, C., Belur Nagaraj, S., van Putten, M. J. A. M. & Seifert, C. Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology ReportsIn 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI), Article 9319371 (pp. 72-81). IEEE. Nguyen, E., Theodorakopoulos, D., Pathak, S., Geerdink, J., Vijlbrief, O., van Keulen, M. & Seifert, C.
Human-in-the-loop Language-agnostic Extraction of Medication Data from Highly Unstructured Electronic Health RecordsIn 2020 International Conference on Data Mining Workshops (ICDMW), Article 9346382 (pp. 644-650). IEEE. Ruis, F., Pathak, S., Geerdink, J., Hegeman, J. H., Seifert, C. & van Keulen, M.

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