Exploring deep generative models for improved data generation in hypertrophic cardiomyopathy
Data generation strategies are essential for addressing the challenge of limited training data in deep learning-based medical image analysis, particularly for hypertrophic cardiomyopathy (HCM) using magnetic resonance imaging (MRI). Unlike traditional augmentation techniques, deep generative models...
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| Veröffentlicht in: | INGENIUS H. 34; S. 116 - 125 |
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Universidad Politécnica Salesiana del Ecuador
01.07.2025
Universidad Politécnica Salesiana |
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| Abstract | Data generation strategies are essential for addressing the challenge of limited training data in deep learning-based medical image analysis, particularly for hypertrophic cardiomyopathy (HCM) using magnetic resonance imaging (MRI). Unlike traditional augmentation techniques, deep generative models can synthesize novel and diverse MRI images, enriching the training datasets. This study evaluates several generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Deep Convolutional GANs (DCGANs), Auxiliary Classifier GANs (ACGANs), InfoGANs, and Diffusion Models, using the Structural Similarity Index Measure (SSIM) and Cross-Correlation Coefficient (CC) to assess image quality and structural fidelity. While VAEs demonstrated limitations such as noticeable noise and blurriness, GAN-based models, particularly DCGANs and ACGANs, generated higher-quality and anatomically accurate images. Diffusion models achieved the highest image fidelity among all the methods evaluated, but required longer generation times. These findings underscore the trade-off between image quality and computational efficiency and highlight the potential of deep generative models to augment MRI datasets, thereby improving deep learning applications for HCM diagnosis.
Las estrategias de generación de datos son fundamentales para superar el desafío de los datos de entrenamiento limitados en el análisis de imágenes médicas basado en aprendizaje profundo, en particular para la miocardiopatía hipertrófica (HCM) mediante resonancia magnética (MRI). A diferencia de los métodos de aumento tradicionales, los modelos generativos profundos pueden sintetizar imágenes de MRI novedosas y diversas. Este estudio evalúa múltiples modelos generativos: autocodificadores variacionales (VAE), redes generativas adversarias (GAN), GAN convolucionales profundas (DCGAN), GAN con clasificador auxiliar (ACGAN), InfoGAN y modelos de difusión, utilizando el índice de similitud estructural (SSIM) y el coeficiente de correlación cruzada (CC) para evaluar la calidad de imagen y la fidelidad estructural. Si bien los VAE mostraron limitaciones como el ruido y la borrosidad, los modelos basados en GAN, especialmente DCGAN y ACGAN, produjeron imágenes de mayor calidad y precisión anatómica. Los modelos de difusión lograron la mayor fidelidad de imagen, aunque a expensas de tiempos de generación más prolongados. Estos resultados destacan la compensación entre la calidad de imagen y la eficiencia computacional, y demuestran el potencial de los modelos generativos para ampliar los conjuntos de datos de MRI, mejorando así las aplicaciones de aprendizaje profundo para el diagnóstico de HCM. |
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| AbstractList | Data generation strategies are essential for addressing the challenge of limited training data in deep learning-based medical image analysis, particularly for hypertrophic cardiomyopathy (HCM) using magnetic resonance imaging (MRI). Unlike traditional augmentation techniques, deep generative models can synthesize novel and diverse MRI images, enriching the training datasets. This study evaluates several generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Deep Convolutional GANs (DCGANs), Auxiliary Classifier GANs (ACGANs), InfoGANs, and Diffusion Models, using the Structural Similarity Index Measure (SSIM) and Cross-Correlation Coefficient (CC) to assess image quality and structural fidelity. While VAEs demonstrated limitations such as noticeable noise and blurriness, GAN-based models, particularly DCGANs and ACGANs, generated higher-quality and anatomically accurate images. Diffusion models achieved the highest image fidelity among all the methods evaluated, but required longer generation times. These findings underscore the trade-off between image quality and computational efficiency and highlight the potential of deep generative models to augment MRI datasets, thereby improving deep learning applications for HCM diagnosis.
Las estrategias de generación de datos son fundamentales para superar el desafío de los datos de entrenamiento limitados en el análisis de imágenes médicas basado en aprendizaje profundo, en particular para la miocardiopatía hipertrófica (HCM) mediante resonancia magnética (MRI). A diferencia de los métodos de aumento tradicionales, los modelos generativos profundos pueden sintetizar imágenes de MRI novedosas y diversas. Este estudio evalúa múltiples modelos generativos: autocodificadores variacionales (VAE), redes generativas adversarias (GAN), GAN convolucionales profundas (DCGAN), GAN con clasificador auxiliar (ACGAN), InfoGAN y modelos de difusión, utilizando el índice de similitud estructural (SSIM) y el coeficiente de correlación cruzada (CC) para evaluar la calidad de imagen y la fidelidad estructural. Si bien los VAE mostraron limitaciones como el ruido y la borrosidad, los modelos basados en GAN, especialmente DCGAN y ACGAN, produjeron imágenes de mayor calidad y precisión anatómica. Los modelos de difusión lograron la mayor fidelidad de imagen, aunque a expensas de tiempos de generación más prolongados. Estos resultados destacan la compensación entre la calidad de imagen y la eficiencia computacional, y demuestran el potencial de los modelos generativos para ampliar los conjuntos de datos de MRI, mejorando así las aplicaciones de aprendizaje profundo para el diagnóstico de HCM. Data generation strategies are essential for addressing the challenge of limited training data in deep learning-based medical image analysis, particularly for hypertrophic cardiomyopathy (HCM) using magnetic resonance imaging (MRI). Unlike traditional augmentation techniques, deep generative models can synthesize novel and diverse MRI images, enriching the training datasets. This study evaluates several generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Deep Convolutional GANs (DCGANs), Auxiliary Classifier GANs (ACGANs), InfoGANs, and Diffusion Models, using the Structural Similarity Index Measure (SSIM) and Cross-Correlation Coefficient (CC) to assess image quality and structural fidelity. While VAEs demonstrated limitations such as noticeable noise and blurriness, GAN-based models, particularly DCGANs and ACGANs, generated higher-quality and anatomically accurate images. Diffusion models achieved the highest image fidelity among all the methods evaluated, but required longer generation times. These findings underscore the trade-off between image quality and computational efficiency and highlight the potential of deep generative models to augment MRI datasets, thereby improving deep learning applications for HCM diagnosis. |
| Author | Rao, Gottapu Sasibhushana Rayavarapu, Swarajya Madhuri |
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| Title | Exploring deep generative models for improved data generation in hypertrophic cardiomyopathy |
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