M. Nauta MSc (Meike)

Nauta, M., Trienes, J. , Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J. , van Keulen, M. , & Seifert, C. (2022). From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ArXiv.org. https://doi.org/10.48550/arXiv.2201.08164

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, [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 Science + Business Media. https://doi.org/10.1007/978-3-030-93736-2_34

Nauta, M., Walsh, R., Dubowski, A. , & Seifert, C. (2022). Uncovering and Correcting Shortcut Learning in Machine Learning Models for Skin Cancer Diagnosis. Diagnostics, 12(1), [40]. https://doi.org/10.3390/diagnostics12010040

Nauta, M., van Bree, R. , & Seifert, C. (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). IEEE. https://doi.org/10.1109/CVPR46437.2021.01469

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 (pp. 5-11). (CEUR Workshop Proceedings; Vol. 2675). CEUR. http://ceur-ws.org/Vol-2675/

Theodorus, A. , Nauta, M. , & Seifert, C. (2020). Evaluating CNN interpretability on sketch classification. In W. Osten, D. Nikolaev, & J. Zhou (Eds.), 12th International Conference on Machine Vision, ICMV 2019 [114331Q] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11433). SPIE Press. https://doi.org/10.1117/12.2559536

Peters, M., Kempen, L. , Nauta, M. , & Seifert, C. (2019). Visualising the Training Process of Convolutional Neural Networks for Non-Experts. Paper presented at 31st Benelux Conference on Artificial Intelligence, BNAIC 2019, Brussels, Belgium.

Nauta, M. , Bucur, D. , & Seifert, C. (2019). Causal Discovery with Attention-Based Convolutional Neural Networks. Machine Learning and Knowledge Extraction, 1(1), 312-340. https://doi.org/10.3390/make1010019