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 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.