PaperSummary: Using Diffusion Models to Generate Synthetic Labeled Data for Medical Image Segmentation

The paper explores the use of diffusion models to generate synthetic labeled data for medical image segmentation, addressing the challenge of limited high-quality, annotated datasets due to privacy laws and resource constraints. Key points:

1. Pipeline Design: Diffusion models are trained on the HyperKvasir dataset to generate synthetic polyp images and segmentation masks. This process includes clustering, inpainting, and styling to ensure the synthetic data has high fidelity and diversity.
2. Evaluation: The synthetic data is assessed through qualitative expert review, Fréchet Inception Distance (FID), and Multi-Scale Structural Similarity (MS-SSIM). These images demonstrate improved segmentation performance over GANs and effective generalization to other datasets.
3. Segmentation Models: Models trained on synthetic data, especially styled data, show competitive or superior performance compared to real data, particularly in small dataset scenarios.
4. Transferability: The generated data improves performance when applied to different datasets, indicating strong generalizability.
5. Advantages: The pipeline automates data generation, reducing annotation effort, and is computationally efficient. The inclusion of segmentation masks as a fourth channel avoids additional model requirements.
6. Limitations and Future Work: The study is constrained by the dataset and computational resources. Future exploration may include applying the method to other datasets and integrating differential privacy techniques for broader data sharing.

This approach enhances medical imaging workflows by augmenting datasets with realistic synthetic data, improving model training while conserving resources.

References:

https://arxiv.org/abs/2310.16794