Explainable Deep Learning for Personalized Age Prediction with Brain Morphology

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021-05

Autores

Angela Lombardi (Autor) Outra; Domenico Diacono (Autor) Outra; Nicola Amoroso (Autor) Outra; Alfonso Monaco (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP; Roberto Bellotti (Autor) Outra; Sabina Tangaro (Autor) Outra

Revista

Título: Frontiers in Systems Neuroscience

Vol. 15 Nº Art.674055

Páginas: 1-17

ISSN: 1662-5137

Editora: Frontiers Media

Indexação

ISI Web of Knowledge

ISI Web of Science

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências médicas e da saúde

Outras Informações

DOI: 10.3389/fnins.2021.674055

Abstract (EN): Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker. © Copyright © 2021 Lombardi, Diacono, Amoroso, Monaco, Tavares, Bellotti and Tangaro.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 17