Subspace corrected relevance learning with application in neuroimaging

Date

2024

Authors

Veen, Rick van
Bari Tamboli, Neha Rajendra
Lövdal, Sofie
Meles, Sanne K.
Renken, Remco J.
Vries, Gert-Jan de
Arnaldi, Dario
Morbelli, Silvia
Obeso, José A.

Director

Publisher

Elsevier
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

Impacto
No disponible en Scopus

Abstract

In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson’s disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a “relevance space” that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system’s training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate “relevance space” can be identified to construct the correction matrix.

Description

Keywords

Learning vector quantization, Relevance learning, Generalized Matrix Learning Vector Quantization (GMLVQ), Multi-source data, Neuroimaging

Department

Ciencias de la Salud / Osasun Zientziak

Faculty/School

Degree

Doctorate program

item.page.cita

Van Veen, R., Tamboli, N. R. B., Lövdal, S., Meles, S. K., Renken, R. J., de Vries, G.-J., Arnaldi, D., Morbelli, S., Clavero, P., Obeso, J. A., Oroz, M. C. R., Leenders, K. L., Villmann, T., Biehl, M. (2024) Subspace corrected relevance learning with application in neuroimaging. Artificial Intelligence in Medicine, 149, 1-12. https://doi.org/10.1016/j.artmed.2024.102786.

item.page.rights

© 2024 The Author(s). This is an open access article under the CC BY license.

Licencia

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