Investigating the discrimination ability of 3D convolutional neural networks applied to altered brain MRI parametric maps.

Autor: Mattia GM; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France. Electronic address: giulia-maria.mattia@inserm.fr., Villain E; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France; LAAS CNRS, Université de Toulouse, CNRS, INSA, UPS, Toulouse, France. Electronic address: villain.edouard@outlook.fr., Nemmi F; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France. Electronic address: federico.nemmi@gmail.com., Le Lann MV; LAAS CNRS, Université de Toulouse, CNRS, INSA, UPS, Toulouse, France. Electronic address: mvlelann@laas.fr., Franceries X; CRCT, Centre de Recherche en Cancérologie de Toulouse, Inserm, UPS, Toulouse, France. Electronic address: xavier.franceries@inserm.fr., Péran P; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, Toulouse, France. Electronic address: patrice.peran@inserm.fr.
Jazyk: angličtina
Zdroj: Artificial intelligence in medicine [Artif Intell Med] 2024 Jul; Vol. 153, pp. 102897. Date of Electronic Publication: 2024 May 17.
DOI: 10.1016/j.artmed.2024.102897
Abstrakt: Convolutional neural networks (CNNs) are gradually being recognized in the neuroimaging community as a powerful tool for image analysis. Despite their outstanding performances, some aspects of CNN functioning are still not fully understood by human operators. We postulated that the interpretability of CNNs applied to neuroimaging data could be improved by investigating their behavior when they are fed data with known characteristics. We analyzed the ability of 3D CNNs to discriminate between original and altered whole-brain parametric maps derived from diffusion-weighted magnetic resonance imaging. The alteration consisted in linearly changing the voxel intensity of either one (monoregion) or two (biregion) anatomical regions in each brain volume, but without mimicking any neuropathology. Performing ten-fold cross-validation and using a hold-out set for testing, we assessed the CNNs' discrimination ability according to the intensity of the altered regions, comparing the latter's size and relative position. Monoregion CNNs showed that the larger the modified region, the smaller the intensity increase needed to achieve good performances. Biregion CNNs systematically outperformed monoregion CNNs, but could only detect one of the two target regions when tested on the corresponding monoregion images. Exploiting prior information on training data allowed for a better understanding of CNN behavior, especially when altered regions were combined. This can inform about the complexity of CNN pattern retrieval and elucidate misclassified examples, particularly relevant for pathological data. The proposed analytical approach may serve to gain insights into CNN behavior and guide the design of enhanced detection systems exploiting our prior knowledge.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: