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 | 209 | 2021 |
Causal discovery with attention-based convolutional neural networks M Nauta, D Bucur, C Seifert Machine Learning and Knowledge Extraction 1 (1), 19, 2019 | 207 | 2019 |
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 | 199 | 2023 |
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 | 57 | 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 | 52 | 2021 |
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 |
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, 110786, 2023 | 28 | 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 | 28 | 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, 110787, 2023 | 26 | 2023 |
Detecting Hacked Twitter Accounts based on Behavioural Change. M Nauta, MB Habib, M van Keulen WEBIST, 19-31, 2017 | 21* | 2017 |
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 | 6 | 2023 |
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 |
Benchmarking eXplainable AI: a survey on available toolkits and open challenges PQ Le, M Nauta, VB Nguyen, S Pathak, J Schlötterer, C Seifert International Joint Conference on Artificial Intelligence, 2023 | 5 | 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 | 5 | 2022 |
The co-12 recipe for evaluating interpretable part-prototype image classifiers M Nauta, C Seifert World Conference on Explainable Artificial Intelligence, 397-420, 2023 | 4 | 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 | 4 | 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, 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 |