Expertise
Computer Science
- Models
- Prediction Model
- Explainable Artificial Intelligence
- Chain Rule
- Preoperative Data
- Machine Learning
Medicine and Dentistry
- Hip Fracture
- Surgical Mortality
Organisations
Publications
2024
Feature Importance to Explain Multimodal Prediction Models: a Clinical Use Case (2024)In Explainable Artificial Intelligence: Second World Conference, xAI 2024. Valletta, Malta, July 17-19, 2024. Proceedings, Part IV (pp. 84-101) (Communications in Computer and Information Science; Vol. 2156). Springer Nature. van de Beld, J.-J., Pathak, S., Geerdink, J., Hegeman, J. H. & Seifert, C.https://doi.org/10.1007/978-3-031-63803-9_5Prototype-Based Interpretable Breast Cancer Prediction Models: Analysis and Challenges (2024)In Explainable Artificial Intelligence - 2nd World Conference, xAI 2024, Proceedings (pp. 21-42) (Communications in Computer and Information Science; Vol. 2153 CCIS). Springer. Pathak, S., Schlötterer, J., Veltman, J., Geerdink, J., van Keulen, M. & Seifert, C.https://doi.org/10.1007/978-3-031-63787-2_2Feature importance to explain multimodal prediction models. A clinical use case (2024)[Working paper › Preprint]. ArXiv.org. van de Beld, J.-J., Pathak, S., Geerdink, J., Hegeman, J. H. & Seifert, C.Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges (2024)[Working paper › Preprint]. Pathak, S., Schlötterer, J., Veltman, J., Geerdink, J., Keulen, M. v. & Seifert, C.
2023
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023)ACM 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.https://doi.org/10.1145/3583558Case-level Breast Cancer Prediction for Real Hospital Settings (2023)[Working paper › Preprint]. ArXiv.org. Pathak, S., Schlötterer, J., Geerdink, J., Veltman, J., van Keulen, M., Strisciuglio, N. & Seifert, C.https://doi.org/10.48550/arXiv.2310.12677Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings (2023)[Working paper › Preprint]. ArXiv.org. Pathak, S., Schlötterer, J., Geerdink, J., Vijlbrief, O. D., Keulen, M. v. & Seifert, C.Benchmarking eXplainable AI: A Survey on Available Toolkits and Open Challenges (2023)In Proceedings of the 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023 (pp. 6665-6673) (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2023-August). International Joint Conferences on Artificial Intelligence. Le, P. Q., Nauta, M., Nguyen, V. B., Pathak, S., Schlötterer, J. & Seifert, C.
2022
Channel Contribution In Deep Learning Based Automatic Sleep Scoring – How Many Channels Do We Need? (2022)IEEE transactions on neural systems and rehabilitation engineering, 31, 494-505. Lu, C., Pathak, S., Englebienne, G. & Seifert, C.https://doi.org/10.1109/TNSRE.2022.3227040From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2022)[Working paper › Preprint]. ArXiv.org. Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M. & Seifert, C.https://doi.org/10.48550/arXiv.2201.08164
Research profiles
Address
University of Twente
Zilverling (building no. 11), room 4057
Hallenweg 19
7522 NH Enschede
Netherlands
University of Twente
Zilverling 4057
P.O. Box 217
7500 AE Enschede
Netherlands
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