Balancing Real and Synthetic Data in Computer Vision Model Training for Medical Applications

Parshv praful Gala¹ & Vikhyat Gupta²

¹Carnegie Mellon University

Pittsburgh, USA

gala4296@gmail.com

²Chandigarh University

Punjab, India

vishutayal18@gmail.com

Abstract– The tremendous progress in computer vision and deep learning has tremendously influenced medical imaging, leading to enhanced diagnostic precision and automation. Yet, the scarcity of large-scale, annotated medical image datasets has long been a significant impediment to developing robust models for clinical use. In order to overcome this, the adoption of synthetic data, created via methods like Generative Adversarial Networks (GANs) and simulation-based approaches, has emerged as an exciting solution. Synthetic data has the potential to augment sparse real-world datasets, enhance generalizability, and address class imbalance in medical image classification and segmentation. Despite the benefits, several challenges arise with the use of synthetic data, including domain shift, the propagation of biases, and the potential for overfitting. A balance between real and synthetic data during training is essential to avoid these drawbacks. Recent work has emphasized the hybrid approach to combine both real and synthetic datasets and demonstrate enhanced model performance, generalization, and stability in a range of medical imaging applications, from tumor detection to rare disease detection, and multi-modal imaging tasks. In addition, ethical aspects such as fairness, privacy, and bias reduction play important roles when utilizing synthetic data for clinical applications. The evolution of synthetic data generation methods, hybrid training, and their applications in medical imaging are examined in this review with the focus on obtaining the optimal ratio between real and synthetic data. Future studies must continue to evolve these methods and address the hurdles of achieving high-quality, representative, and unbiased synthetic data for effective clinical use.

Keywords— synthetic data, real data, medical imaging, deep learning, generative adversarial networks (GANs), hybrid models, data augmentation, class imbalance, domain shift, medical image segmentation

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