Detecting Hacked Twitter Accounts based on Behavioural Change. - Meike Nauta; Mena B. Habib; Maurice van Keulen (WEBIST, 2017)

LIFT: Learning Fault Trees from Observational Data. - Meike Nauta; Doina Bucur; Mariëlle Stoelinga (QEST, 2018)

Visualising the Training Process of Convolutional Neural Networks for Non-Experts. - Michelle Peters; Lindsay Kempen; Meike Nauta; Christin Seifert (BNAIC/BENELEARN, 2019)

Evaluating CNN interpretability on sketch classification. - Abraham Theodorus; Meike Nauta; Christin Seifert (ICMV, 2019)

Causal Discovery with Attention-Based Convolutional Neural Networks. - Meike Nauta; Doina Bucur; Christin Seifert (Mach. Learn. Knowl. Extr., 2019)

Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients. - Meike Nauta; Michel J. A. M. van Putten; Marleen C. Tjepkema-Cloostermans; Jeroen Bos; Maurice van Keulen; Christin Seifert (KDH@ECAI, 2020)

Neural Prototype Trees for Interpretable Fine-Grained Image Recognition. - Meike Nauta; Ron van Bree; Christin Seifert (CVPR, 2021)

This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition. - Meike Nauta; Annemarie Jutte; Jesper C. Provoost; Christin Seifert (PKDD/ECML Workshops (1), 2021)

Radiology report generation for proximal femur fractures using deep classification and language generation models. - Olivier Paalvast; Meike Nauta; Marion Koelle; Jeroen Geerdink; Onno Vijlbrief; Johannes H. Hegeman; Christin Seifert (Artif. Intell. Medicine, 2022)

Feature Attribution Explanations for Spiking Neural Networks. - Elisa Nguyen; Meike Nauta; Gwenn Englebienne; Christin Seifert (CogMI, 2023)

PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. - Meike Nauta; Jörg Schlötterer; Maurice van Keulen; Christin Seifert (CVPR, 2023)

Interpreting and Correcting Medical Image Classification with PIP-Net. - Meike Nauta; Johannes H. Hegeman; Jeroen Geerdink; Jörg Schlötterer; Maurice van Keulen; Christin Seifert (ECAI Workshops (1), 2023)

Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges. - Phuong Quynh Le; Meike Nauta; Van Bach Nguyen; Shreyasi Pathak; Jörg Schlötterer; Christin Seifert (IJCAI, 2023)

The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers. - Meike Nauta; Christin Seifert (xAI (1), 2023)

Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN. - Una M. Kelly; Meike Nauta; Lu Liu; Luuk J. Spreeuwers; Raymond N. J. Veldhuis (CoRR, 2023)

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. - Meike Nauta; Jan Trienes; Shreyasi Pathak; Elisa Nguyen; Michelle Peters; Yasmin Schmitt; Jörg Schlötterer; Maurice van Keulen; Christin Seifert (ACM Comput. Surv., 2023)

PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans. - Lisa Anita De Santi; Jörg Schlötterer; Michael Scheschenja; Joel Wessendorf; Meike Nauta; Vincenzo Positano; Christin Seifert (ISIC/iMIMIC/EARTH/DeCaF@MICCAI, 2024)

Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification. - Lisa Anita De Santi; Jörg Schlötterer; Meike Nauta; Vincenzo Positano; Christin Seifert (xAI (Late-breaking Work, Demos, Doctoral Consortium), 2024)