Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

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 (T...

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Bibliographic Details
Authors: Soliman, A, Chang, JR, Etminani, K, Byttner, S, Davidsson, A, Martinez-Sanchis, B, Camacho, V, Bauckneht, M, Stegeran, R, Ressner, M, Agudelo-Cifuentes, M, Chincarini, A, Brendel, M, Rominger, A, Bruffaerts, R, Vandenberghe, R, Kramberger, MG, Trost, M, Nicastro, N, Frisoni, GB, Lemstra, AW, van Berckel, BNM, Pilotto, A, Padovani, A, Morbelli, S, Aarsland, D, Nobili, F, Garibotto, V, Ochoa-Figueroa, M
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)
Repository:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau
OAI Identifier:oai:iibsantpau.fundanetsuite.com:p15459
Online Access:https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15459
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143570393&doi=10.1186%2fs12911-022-02054-7&partnerID=40&md5=7c9d01e7906101356f8f05eb6a4b1d6a
Access Level:Open access
Keyword:Convolution Neural Networks
Transfer Learning
Brain Neurodegenerative Disorders
Medical Image Classification
Description
Summary: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.

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