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Voxel-Level Importance Maps for Interpretable Brain Age Estimation

Bintsi KM, Baltatzis V, Hammers A, Rueckert D (2021)

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Publication Type: Conference contribution

Publication year: 2021

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12929 LNCS

Pages Range: 65-74

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Strasbourg, FRA

ISBN: 9783030874438

DOI: 10.1007/978-3-030-87444-5_7

Abstract

Brain aging, and more specifically the difference between the chronological and the biological age of a person, may be a promising biomarker for identifying neurodegenerative diseases. For this purpose accurate prediction is important but the localisation of the areas that play a significant role in the prediction is also crucial, in order to gain clinicians’ trust and reassurance about the performance of a prediction model. Most interpretability methods are focused on classification tasks and cannot be directly transferred to regression tasks. In this study, we focus on the task of brain age regression from 3D brain Magnetic Resonance (MR) images using a Convolutional Neural Network, termed prediction model. We interpret its predictions by extracting importance maps, which discover the parts of the brain that are the most important for brain age. In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition. We implement a noise model which aims to add as much noise as possible to the input without harming the performance of the prediction model. We average the importance maps of the subjects and end up with a population-based importance map, which displays the regions of the brain that are influential for the task. We test our method on 13,750 3D brain MR images from the UK Biobank, and our findings are consistent with the existing neuropathology literature, highlighting that the hippocampus and the ventricles are the most relevant regions for brain aging.

Involved external institutions

How to cite

APA:

Bintsi, K.-M., Baltatzis, V., Hammers, A., & Rueckert, D. (2021). Voxel-Level Importance Maps for Interpretable Brain Age Estimation. In Mauricio Reyes, Pedro Henriques Abreu, Jaime Cardoso, Mustafa Hajij, Ghada Zamzmi, Paul Rahul, Lokendra Thakur (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 65-74). Strasbourg, FRA: Springer Science and Business Media Deutschland GmbH.

MLA:

Bintsi, Kyriaki-Margarita, et al. "Voxel-Level Importance Maps for Interpretable Brain Age Estimation." Proceedings of the 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020 and 1st International Workshop on Topological Data Analysis and Its Applications for Medical Data, TDA4MedicalData 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Strasbourg, FRA Ed. Mauricio Reyes, Pedro Henriques Abreu, Jaime Cardoso, Mustafa Hajij, Ghada Zamzmi, Paul Rahul, Lokendra Thakur, Springer Science and Business Media Deutschland GmbH, 2021. 65-74.

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