Unrealistic Data Augmentation Improves the Robustness of Deep Learning-Based Classification of Dopamine Transporter SPECT Against Variability Between Sites and Between Cameras.
J Nucl Med
; 65(9): 1463-1466, 2024 Sep 03.
Article
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| MEDLINE
| ID: mdl-39054285
ABSTRACT
We propose strongly unrealistic data augmentation to improve the robustness of convolutional neural networks (CNNs) for automatic classification of dopamine transporter SPECT against the variability between sites and between cameras. Methods:
A CNN was trained on a homogeneous dataset comprising 1,100 123I-labeled 2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images using strongly unrealistic data augmentation based on gaussian blurring and additive noise. Strongly unrealistic data augmentation was compared with no augmentation and intensity-based nnU-Net augmentation on 2 independent datasets with lower (n = 645) and considerably higher (n = 640) spatial resolution.Results:
The CNN trained with strongly unrealistic augmentation achieved an overall accuracy of 0.989 (95% CI, 0.978-0.996) and 0.975 (95% CI, 0.960-0.986) in the independent test datasets, which was better than that without (0.960, 95% CI, 0.942-0.974; 0.953, 95% CI, 0.934-0.968) and with nnU-Net augmentation (0.972, 95% CI, 0.956-0.983; 0.950, 95% CI, 0.930-0.966) (all McNemar P < 0.001).Conclusion:
Strongly unrealistic data augmentation results in better generalization of CNN-based classification of 123I-labeled 2ß-carbomethoxy-3ß-(4-iodophenyl)-N-(3-fluoropropyl)nortropane SPECT images to unseen acquisition settings. We hypothesize that this can be transferred to other nuclear imaging applications.Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Tomografía Computarizada de Emisión de Fotón Único
/
Proteínas de Transporte de Dopamina a través de la Membrana Plasmática
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Aprendizaje Profundo
Límite:
Humans
Idioma:
En
Revista:
J Nucl Med
Año:
2024
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Estados Unidos