Abstract

Background

In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans.

Results

Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis.

Conclusions

TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

Details

Title
Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model
Author
Soliman, Amira; Chang, Jose R; Etminani, Kobra; Byttner, Stefan; Davidsson, Anette; Martínez-Sanchis, Begoña; Camacho, Valle; Bauckneht, Matteo; Stegeran, Roxana; Ressner, Marcus; Agudelo-Cifuentes, Marc; Chincarini, Andrea; Brendel, Matthias; Rominger, Axel; Rose Bruffaerts; Vandenberghe, Rik
Pages
1-16
Section
Research
Publication year
2022
Publication date
2022
Publisher
BioMed Central
e-ISSN
14726947
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2755585458
Copyright
© 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.