»It is hard to imagine a person who would feel comfortable in blindly agreeing with a system’s decision without a deep understanding of the decision-making rationale.
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Detailed explanations of AI decisions seem necessary to provide insight into the rationale the AI uses to draw a conclusion.«

Derek Doran, 2017

»Only when users and stakeholders understand how and what predictions AI systems arrive at these systems can be used responsibly to make important decisions.«

Simon B. Eickhoff, 2021

Welcome!

This site is dedicated to my research on explainable artificial intelligence, driven by the vision that machine learning models should not only make decisions or provide probabilities, but also provide human-understandable explanations for how they reach their conclusions.

Accuracy

Machine learning models must be accurate to be able to provide reasonable explanations.
We validate all models in independent datasets to test their performance.

Focus

Machine learning models need to focus on key features that are causally related to the target and often known a priori.
Relevance maps help us to assess the level of noise and to detect bias in the training data.

Explanation

Machine learning models should provide an explanation describing the decision-making process in an intuitive way.
Our goal is to develop a system architecture that can extract relevant information and dynamically synthesize descriptive explanations.

Martin Dyrba, PhD

I am a researcher in artificial intelligence and translational research in neuroimaging. My research interests are machine learning methods to detect neurodegenerative diseases such as Alzheimer’s.

At present, I am research associate at the German Center for Neurodegenerative Diseases (DZNE), Rostock. Here, I work on novel approaches to improve the comprehensibility and interpretability of machine learning models.

Biosketch & activities

In 2011, I graduated from the University of Rostock. In 2016, I obtained my PhD in medical informatics and, in 2024, I completed my habilitation (venia legendi). At the end of 2015, I was awarded the Steinberg-Krupp Alzheimer’s Research Prize for my work on machine learning models to detect Alzheimer’s disease based on multicenter neuroimaging data. I have been working as reviewer for several grant agencies and international journals. I was guest editor for the Frontiers Research Topic ‘Deep Learning in Aging Neuroscience’ and ‘AI and Machine Learning for Cognitive Decline: From Benchmarks to Clinical Practice’.

Deep learning InteractiveVis

In 2020, I developed a convolutional neural network architecture to detect Alzheimer’s disease in MRI scans. The diagnostic performance was validated in three independent cohorts.

From the neural networks, we can derive relevance maps that indicate the brain areas with high contribution to the diagnostic decision. Medial temporal lobe atrophy was shown as most relevant area which matched our expectations, as hippocampus volume is actually the best established neuroimaging marker for Alzheimer’s disease.

Obtaining explanations from neural networks

We are working on a system architecture that can extract relevant information and dynamically generate descriptive explanations of varying granularity on demand.

“AI will drastically change healthcare. We are working on making AI systems more reliable, transparent, and comprehensible.”