| From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai M Nauta, J Trienes, S Pathak, E Nguyen, M Peters, Y Schmitt, ... ACM Computing Surveys 55 (13s), 1-42, 2023 | 254 | 2023 |
| Neural prototype trees for interpretable fine-grained image recognition M Nauta, R Van Bree, C Seifert Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 230 | 2021 |
| Causal discovery with attention-based convolutional neural networks M Nauta, D Bucur, C Seifert Machine Learning and Knowledge Extraction 1 (1), 19, 2019 | 222 | 2019 |
| This looks like that, because... explaining prototypes for interpretable image recognition M Nauta, A Jutte, J Provoost, C Seifert Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 61 | 2021 |
| Uncovering and correcting shortcut learning in machine learning models for skin cancer diagnosis M Nauta, R Walsh, A Dubowski, C Seifert Diagnostics 12 (1), 40, 2021 | 57 | 2021 |
| Explainable AI in medical imaging: An overview for clinical practitioners–Beyond saliency-based XAI approaches K Borys, YA Schmitt, M Nauta, C Seifert, N Krämer, CM Friedrich, F Nensa European journal of radiology 162, 110786, 2023 | 42 | 2023 |
| Pip-net: Patch-based intuitive prototypes for interpretable image classification M Nauta, J Schlötterer, M Van Keulen, C Seifert Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 34 | 2023 |
| Explainable ai in medical imaging: An overview for clinical practitioners–saliency-based xai approaches K Borys, YA Schmitt, M Nauta, C Seifert, N Krämer, CM Friedrich, F Nensa European journal of radiology 162, 110787, 2023 | 33 | 2023 |
| LIFT: learning fault trees from observational data M Nauta, D Bucur, M Stoelinga Quantitative Evaluation of Systems: 15th International Conference, QEST 2018 …, 2018 | 30 | 2018 |
| Detecting hacked twitter accounts based on behavioural change M Nauta, MB Habib, M van Keulen 13th International Conference on Web Information Systems and Technologies …, 2017 | 22* | 2017 |
| Benchmarking eXplainable AI-A Survey on Available Toolkits and Open Challenges. PQ Le, M Nauta, SP Van Bach Nguyen, S Pathak, J Schlötterer, C Seifert IJCAI, 6665-6673, 2023 | 8 | 2023 |
| Interpreting and correcting medical image classification with pip-net M Nauta, JH Hegeman, J Geerdink, J Schlötterer, M Keulen, C Seifert European Conference on Artificial Intelligence, 198-215, 2023 | 7 | 2023 |
| Radiology report generation for proximal femur fractures using deep classification and language generation models O Paalvast, M Nauta, M Koelle, J Geerdink, O Vijlbrief, JH Hegeman, ... Artificial intelligence in medicine 128, 102281, 2022 | 6 | 2022 |
| Evaluating CNN interpretability on sketch classification A Theodorus, M Nauta, C Seifert Twelfth International Conference on Machine Vision (ICMV 2019) 11433, 475-482, 2020 | 6 | 2020 |
| The co-12 recipe for evaluating interpretable part-prototype image classifiers M Nauta, C Seifert World Conference on Explainable Artificial Intelligence, 397-420, 2023 | 5 | 2023 |
| Interactive explanations of internal representations of neural network layers: An exploratory study on outcome prediction of comatose patients M Nauta, MJAM van Putten, MC Tjepkema-Cloostermans, JP Bos, ... 5th International Workshop on Knowledge Discovery in Healthcare Data, KDH …, 2020 | 5 | 2020 |
| Visualising the training process of convolutional neural networks for non-experts M Peters, L Kempen, M Nauta, C Seifert 31st Benelux Conference on Artificial Intelligence, BNAIC 2019, 2019 | 3 | 2019 |
| Explainable ai and interpretable computer vision: From oversight to insight M Nauta | 2 | 2023 |
| Worst‐Case Morphs Using Wasserstein ALI and Improved MIPGAN UM Kelly, M Nauta, L Liu, LJ Spreeuwers, RNJ Veldhuis IET biometrics 2023 (1), 9353816, 2023 | 1 | 2023 |
| PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans LA De Santi, J Schlötterer, M Scheschenja, J Wessendorf, M Nauta, ... arXiv preprint arXiv:2403.18328, 2024 | | 2024 |
Christin SeifertUniversity of Marburg / Hessian.AI在 uni-marburg.de 的电子邮件经过验证
