M. Nauta MSc (Meike)

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.

Nauta, M., Schlötterer, J. , van Keulen, M. , & Seifert, C. (2023). PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. In CVPR 2023 (pp. 2744-2753)

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, [110786]. https://doi.org/10.1016/j.ejrad.2023.110786

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. 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.