Meike Nauta

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 24

Top Topics

Convolutional Neural Networks

Evaluation Methods

Top Venues

Artif. Intell. Medicine

Publications

Lisa Anita De Santi, Jörg Schlötterer, Michael Scheschenja, Joel Wessendorf, Meike Nauta, Vincenzo Positano, Christin Seifert
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans. CoRR (2024)
Lisa Anita De Santi, Jörg Schlötterer, Meike Nauta, Vincenzo Positano, Christin Seifert
Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification. CoRR (2024)
Una M. Kelly, Meike Nauta, Lu Liu, Luuk J. Spreeuwers, Raymond N. J. Veldhuis
Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN. CoRR (2023)
Meike Nauta, Johannes H. Hegeman, Jeroen Geerdink, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
Interpreting and Correcting Medical Image Classification with PIP-Net. ECAI Workshops (1) (2023)
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. ACM Comput. Surv. 55 (13s) (2023)
Meike Nauta, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification. CVPR (2023)
Meike Nauta, Christin Seifert
The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers. xAI (1) (2023)
Elisa Nguyen, Meike Nauta, Gwenn Englebienne, Christin Seifert
Feature Attribution Explanations for Spiking Neural Networks. CogMI (2023)
Elisa Nguyen, Meike Nauta, Gwenn Englebienne, Christin Seifert
Feature Attribution Explanations for Spiking Neural Networks. CoRR (2023)
Meike Nauta, Christin Seifert
The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers. CoRR (2023)
Phuong Quynh Le, Meike Nauta, Van Bach Nguyen, Shreyasi Pathak, Jörg Schlötterer, Christin Seifert
Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges. IJCAI (2023)
Meike Nauta, Johannes H. Hegeman, Jeroen Geerdink, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
Interpreting and Correcting Medical Image Classification with PIP-Net. CoRR (2023)
Olivier Paalvast, Meike Nauta, Marion Koelle, Jeroen Geerdink, Onno Vijlbrief, Johannes H. Hegeman, Christin Seifert
Radiology report generation for proximal femur fractures using deep classification and language generation models. Artif. Intell. Medicine 128 (2022)
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI. CoRR (2022)
Meike Nauta, Ron van Bree, Christin Seifert
Neural Prototype Trees for Interpretable Fine-Grained Image Recognition. CVPR (2021)
Meike Nauta, Annemarie Jutte, Jesper C. Provoost, Christin Seifert
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition. PKDD/ECML Workshops (1) (2021)
Meike Nauta, Annemarie Jutte, Jesper C. Provoost, Christin Seifert
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition. CoRR (2020)
Meike Nauta, Ron van Bree, Christin Seifert
Neural Prototype Trees for Interpretable Fine-grained Image Recognition. CoRR (2020)
Meike Nauta, Michel van Putten, Marleen C. Tjepkema-Cloostermans, Jeroen Bos, Maurice van Keulen, Christin Seifert
Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients. KDH@ECAI (2020)
Abraham Theodorus, Meike Nauta, Christin Seifert
Evaluating CNN interpretability on sketch classification. ICMV (2019)
Meike Nauta, Doina Bucur, Christin Seifert
Causal Discovery with Attention-Based Convolutional Neural Networks. Mach. Learn. Knowl. Extr. 1 (1) (2019)
Michelle Peters, Lindsay Kempen, Meike Nauta, Christin Seifert
Visualising the Training Process of Convolutional Neural Networks for Non-Experts. BNAIC/BENELEARN (2019)
Meike Nauta, Doina Bucur, Mariëlle Stoelinga
LIFT: Learning Fault Trees from Observational Data. QEST (2018)
Meike Nauta, Mena B. Habib, Maurice van Keulen
Detecting Hacked Twitter Accounts based on Behavioural Change. WEBIST (2017)