dr. C. Seifert (Christin)

Guest Associate Professor


Object Recognition
Object Detection
Machine Learning
Reinforcement Learning


Nauta, M. , Seifert, C., & van Bree, R. (2021). Intrinsically Interpretable Image Recognition with Neural Prototype Trees. Abstract from Beyond Fairness: Towards a Just, Equitable, and Accountable Computer Vision, Online Event, .
Nauta, M., van Bree, R. , & Seifert, C. (2021). Neural Prototype Trees for Interpretable Fine-Grained Image Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 14933-14943)
Nguyen, E., Theodorakopoulos, D. , Pathak, S., Geerdink, J., Vijlbrief, O. , van Keulen, M. , & Seifert, C. (2021). A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports. In 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) (pp. 72-81). [9319371] IEEE. https://doi.org/10.1109/CogMI50398.2020.00019
Ruis, F. , Pathak, S., Geerdink, J. , Hegeman, J. H. , Seifert, C. , & van Keulen, M. (2020). Human-in-the-loop Language-agnostic Extraction of Medication Data from Highly Unstructured Electronic Health Records. In 20th International Conference on Data Mining Workshops 2020 IEEE EDS.
Nauta, M. , Putten, M. J. A. M. V. , Tjepkema-Cloostermans, M. C., Bos, J. P. , Keulen, M. V. , & Seifert, C. (2020). Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients. In K. Bach, R. Bunescu, C. Marling, & N. Wiratunga (Eds.), KDH 2020: 5th International Workshop on Knowledge Discovery in Healthcare Data (Vol. 2675, pp. 5-11). (CEUR Workshop Proceedings; Vol. 2675). CEUR. http://ceur-ws.org/Vol-2675/paper1.pdf
Schlötterer, J. , Seifert, C., Satchell, C., & Granitzer, M. (2020). QueryCrumbs Search History Visualization: Usability, Transparency and Long-term Usage. Journal of Computer Languages, 57, [100941]. https://doi.org/10.1016/j.cola.2020.100941
Jan, T., Trienschnigg, D. , Seifert, C. , & Hiemstra, D. (2020). Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records. Paper presented at ACM Health Search and Data Mining Workshop, HSDM 2020, Houston, United States.

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University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling (building no. 11)
Hallenweg 19
7522NH  Enschede
The Netherlands

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University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
P.O. Box 217
7500 AE Enschede
The Netherlands