Synthetic data for deep learning in computer vision & medical imaging: A means to reduce data bias

Analysis

Why This Paper Matters

Deep learning models are notoriously data-hungry, yet many domains—especially medical imaging—suffer from limited, biased datasets. This survey addresses a critical challenge: how synthetic data can be used not only to augment training sets but also to reduce bias that arises from underrepresented groups. By focusing on computer vision and medical imaging, the paper targets areas where data bias can have serious real-world consequences, such as misdiagnosis for certain demographics. The survey's emphasis on bias reduction is timely, as the AI community increasingly recognizes the need for fair and equitable models.

Technical Contributions

• Comprehensive review: The paper surveys a wide range of synthetic data generation methods, including generative adversarial networks (GANs), variational autoencoders (VAEs), and simulation-based techniques, tailored for computer vision and medical imaging.
• Bias mitigation focus: It explicitly connects synthetic data to reducing data bias, discussing how generating diverse synthetic samples can balance datasets and improve model fairness.
• Domain-specific insights: The survey highlights unique considerations in medical imaging, such as privacy constraints and the need for clinically valid synthetic images.
• Challenges and future directions: It outlines open problems, including evaluating synthetic data quality, ensuring diversity, and avoiding introducing new biases.

Results

As a survey, the paper does not present original experimental results. Instead, it synthesizes findings from prior studies, showing that synthetic data can improve model performance when real data is scarce and can help mitigate bias by augmenting underrepresented classes. However, it also notes that synthetic data is not a panacea; poor-quality synthetic data can degrade performance or introduce new biases. The abstract is truncated, so specific metrics and comparisons are not available in this analysis.

Significance

This survey is significant for AI practitioners because it consolidates knowledge on synthetic data generation and its role in bias reduction, providing a foundation for developing more robust and fair deep learning models. In medical imaging, where data sharing is restricted, synthetic data offers a promising avenue to enhance model generalization across diverse populations. By highlighting both successes and pitfalls, the paper encourages responsible adoption of synthetic data and points toward future research needed to standardize evaluation and ensure ethical use. Ultimately, it contributes to the broader goal of trustworthy AI in high-stakes domains.