Abstract
Alzheimer’s disease (AD) has a complex and multifactorial etiology,
which requires integrating information about neuroanatomy, genetics,
and cerebrospinal fluid biomarkers for accurate diagnosis. Hence,
recent deep learning approaches combined image and tabular information
to improve diagnostic performance. However, the black-box nature of
such neural networks is still a barrier for clinical applications, in
which understanding the decision of a heterogeneous model is integral.
We propose PANIC, a prototypical additive neural network for
interpretable AD classification that integrates 3D image and tabular
data. It is interpretable by design and, thus, avoids the need for
post-hoc explanations that try to approximate the decision of a
network. Our results demonstrate that PANIC achieves state-of-the-art
performance in AD classification, while directly providing local and
global explanations. Finally, we show that PANIC extracts biologically
meaningful signatures of AD, and satisfies a set of desirable
desiderata for trustworthy machine learning.
Publication
International Conference on Information Processing in Medical Imaging