The primary objective of this project is to investigate a system architecture and methodologies for deriving explanations from CNN models to reach a self-explanatory, human-comprehensible neural network. We will address the use case of detecting dementia and mild cognitive impairment due to Alzheimer’s disease and frontotemporal lobar degeneration in magnetic resonance imaging (MRI) data.
In 2020, Martin Dyrba 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.