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Recent
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, .
Vries, B. C. S. D.
, Hegeman, J. H.
, Nijmeijer, W.
, Geerdink, J.
, Seifert, C.
, & Groothuis-Oudshoorn, C. G. M. (2021).
Comparing three machine learning approaches to design a risk assessment tool for future fractures: predicting a subsequent major osteoporotic fracture in fracture patients with osteopenia and osteoporosis.
Osteoporosis international,
32(3), 437-449.
https://doi.org/10.1007/s00198-020-05735-z
Libbi, C. A., Trienes, J., Trieschnigg, D.
, & Seifert, C. (2021).
Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records.
Future Internet,
13(5), [136].
https://doi.org/10.3390/fi13050136
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)
Pathak, S.
, Lu, C., Belur Nagaraj, S.
, van Putten, M. J. A. M.
, & Seifert, C. (2021).
STQS: Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring.
Artificial intelligence in medicine,
114, [102038].
https://doi.org/10.1016/j.artmed.2021.102038
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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Zilverling
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University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
P.O. Box 217
7500 AE Enschede
The Netherlands