Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease
This addresses the need for trustworthy machine learning in clinical applications by offering an interpretable model for Alzheimer's disease diagnosis, though it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of interpretable Alzheimer's disease classification by integrating 3D image and tabular data, achieving state-of-the-art performance while providing direct local and global explanations.
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. Our implementation is available at https://github.com/ai-med/PANIC .