544 citations · 599 across the 7 of their papers we have counts for
9 papers
Patch-based Intuitive Multimodal Prototypes Network (PIMPNet) for Alzheimer's Disease classification
Lisa Anita De Santi, Jörg Schlötterer, Meike Nauta +2
Volumetric neuroimaging examinations like structural Magnetic Resonance Imaging (sMRI) are routinely applied to support the clinical diagnosis of dementia like Alzheimer's Disease…
PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans
Lisa Anita De Santi, Jörg Schlötterer, Michael Scheschenja +4
Information from neuroimaging examinations is increasingly used to support diagnoses of dementia, e.g., Alzheimer's disease. While current clinical practice is mainly based on visu…
Feature Attribution Explanations for Spiking Neural Networks
Elisa Nguyen, Meike Nauta, Gwenn Englebienne +1
Third-generation artificial neural networks, Spiking Neural Networks (SNNs), can be efficiently implemented on hardware. Their implementation on neuromorphic chips opens a broad ra…
Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN
Una M. Kelly, Meike Nauta, Lu Liu +2
A morph is a combination of two separate facial images and contains identity information of two different people. When used in an identity document, both people can be authenticate…
The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers
Meike Nauta, Christin Seifert
Interpretable part-prototype models are computer vision models that are explainable by design. The models learn prototypical parts and recognise these components in an image, there…
Interpreting and Correcting Medical Image Classification with PIP-Net
Meike Nauta, Johannes H. Hegeman, Jeroen Geerdink +3
Part-prototype models are explainable-by-design image classifiers, and a promising alternative to black box AI. This paper explores the applicability and potential of interpretable…
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Meike Nauta, Jan Trienes, Shreyasi Pathak +6
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning…
Neural Prototype Trees for Interpretable Fine-grained Image Recognition
Meike Nauta, Ron van Bree, Christin Seifert
Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate…
This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition
Meike Nauta, Annemarie Jutte, Jesper Provoost +1
Image recognition with prototypes is considered an interpretable alternative for black box deep learning models. Classification depends on the extent to which a test image "looks l…