From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
2023 · ACM Computing Surveys · 429 citations
https://doi.org/10.1145/3583558
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Meike Nauta
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Meike Nauta is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Explainable Artificial Intelligence (XAI) & Adversarial Robustness in Machine Learning & Cell Image Analysis Techniques. The author has an h-index of 13, co-authored 28 publications.
ORCID: 0000-0002-0558-3810Papers by this author
worksCausal Discovery with Attention-Based Convolutional Neural Networks
2019 · Machine Learning and Knowledge Extraction · 237 citations
https://doi.org/10.3390/make1010019
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification
2023 · 86 citations
https://doi.org/10.1109/cvpr52729.2023.00269
Radiology report generation for proximal femur fractures using deep classification and language generation models
2022 · Artificial Intelligence in Medicine · 12 citations
https://doi.org/10.1016/j.artmed.2022.102281
Deep Learning for Classification of Pancreatic Cancer Recurrence on CT-imaging
2025 · HPB · 0 citations
https://doi.org/10.1016/j.hpb.2025.07.523
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