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M. Nauta MSc (Meike)

About Me

I am a PhD Candidate at the Data Science group of the University of Twente, the Netherlands. My research interests include explainable artificial intelligence, deep learning, causal discovery and data mining.

Daily life is increasingly governed by decisions made by algorithms due to the growing availability of big data sets. Most machine learning algorithms are black-box models, i.e. they give no insight into how they reach their outcomes which prevents users from trusting the model. If we cannot understand the reasons for their decisions, how can we be sure that the decisions are correct? What if they are wrong, discriminating or amoral?
I aim to create new machine learning methods that can explain their decision making process, in order for users to understand the reasons behind a prediction. Those explanations enable the user to check for correctness, fairness and robustness, and can also be useful for knowledge discovery.

Expertise

Engineering & Materials Science
Convolutional Neural Networks
Decision Making
Deep Learning
Image Recognition
Neural Networks
Radiology
Mathematics
Image Recognition
Interpretability

Publications

Recent
Nauta, M., Schlötterer, J. , van Keulen, M. , & Seifert, C. (2023). PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. Abstract from 2nd Explainable AI for Computer Vision Workshop, XAI4CV 2023, Vancouver, British Columbia, Canada.
Borys, K., Schmitt, Y. A. , Nauta, M. , Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023). Explainable AI in medical imaging: An overview for clinical practitioners - Saliency-based XAI approaches. European journal of radiology, 162, 110787. https://doi.org/10.1016/j.ejrad.2023.110787
Nauta, M. (2023). Explainable AI and Interpretable Computer Vision: From Oversight to Insight. [PhD Thesis - Research UT, graduation UT, University of Twente]. University of Twente. https://doi.org/10.3990/1.9789036555753
Borys, K., Schmitt, Y. A. , Nauta, M. , Seifert, C., Krämer, N., Friedrich, C. M., & Nensa, F. (2023). Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches. European journal of radiology, 162, Article 110786. https://doi.org/10.1016/j.ejrad.2023.110786
Paalvast, O. , Nauta, M., Koelle, M., Geerdink, J., Vijlbrief, O., Hegeman, J. H. , & Seifert, C. (2022). Radiology report generation for proximal femur fractures using deep classification and language generation models. Artificial intelligence in medicine, 128, Article 102281. https://doi.org/10.1016/j.artmed.2022.102281
Nauta, M., Jutte, A., Provoost, J. , & Seifert, C. (2022). This Looks Like That, Because.. Explaining Prototypes for Interpretable Image Recognition. In M. Kamp, M. Kamp, I. Koprinska, A. Bibal, T. Bouadi, B. Frénay, L. Galárraga, J. Oramas, L. Adilova, Y. Krishnamurthy, B. Kang, C. Largeron, J. Lijffijt, T. Viard, P. Welke, M. Ruocco, E. Aune, C. Gallicchio, G. Schiele, F. Pernkopf, M. Blott, H. Fröning, G. Schindler, R. Guidotti, A. Monreale, S. Rinzivillo, P. Biecek, E. Ntoutsi, M. Pechenizkiy, B. Rosenhahn, C. Buckley, D. Cialfi, P. Lanillos, M. Ramstead, T. Verbelen, P. M. Ferreira, G. Andresini, D. Malerba, I. Medeiros, P. Fournier-Viger, M. S. Nawaz, S. Ventura, M. Sun, M. Zhou, V. Bitetta, I. Bordino, A. Ferretti, F. Gullo, G. Ponti, L. Severini, R. Ribeiro, J. Gama, R. Gavaldà, L. Cooper, N. Ghazaleh, J. Richiardi, D. Roqueiro, D. Saldana Miranda, K. Sechidis, ... G. Graça (Eds.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Proceedings (Vol. 1524, pp. 441-456). (Communications in Computer and Information Science; Vol. 1524 CCIS). Springer. https://doi.org/10.1007/978-3-030-93736-2_34

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Contact Details

Visiting Address

University of Twente
Drienerlolaan 5
7522 NB Enschede
The Netherlands

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Mailing Address

University of Twente
P.O. Box 217
7500 AE Enschede
The Netherlands