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. Our implementation is available at https://github.com/ai-med/PANIC.
Dokumententyp: | Konferenzbeitrag (Paper) |
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Fakultät: | Medizin > Klinikum der LMU München > Klinik und Poliklinik für Kinder- und Jugendpsychiatrie, Psychosomatik und Psychotherapie |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
ISBN: | 978-3-031-34047-5 ; 978-3-031-34048-2 |
Ort: | Cham |
Sprache: | Englisch |
Dokumenten ID: | 121968 |
Datum der Veröffentlichung auf Open Access LMU: | 29. Okt. 2024 12:21 |
Letzte Änderungen: | 29. Okt. 2024 12:21 |