用生成模型和伪标签提升医疗影像少样本学习效果
Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework
- 用StyleGAN3生成特定类别图像扩充数据，增强模型泛化能力
- 通过迭代伪标签优化标注，分类与分割性能显著提升
- 适合数据稀缺的医学影像场景，尤其对标注成本高的任务有帮助
医学影像深度学习常受限于标注数据稀少且分布不均。我们提出SSGNet，一种统一框架，结合类别特定生成建模与迭代半监督伪标签，同时提升分类与分割性能。该框架不作为独立模型使用，而是通过StyleGAN3生成图像扩展训练数据，并利用迭代伪标签优化标签。在多个医学影像基准测试中，分类与分割性能均有持续提升；弗雷切特初始距离分析验证了生成样本的高质量。结果表明，SSGNet是一种有效缓解标注瓶颈、提升医学图像分析鲁棒性的实用策略。
原文摘要 · Abstract (English)
Deep learning in medical imaging is often limited by scarce and imbalanced annotated data. We present SSGNet, a unified framework that combines class specific generative modeling with iterative semisupervised pseudo labeling to enhance both classification and segmentation. Rather than functioning as a standalone model, SSGNet augments existing baselines by expanding training data with StyleGAN3 generated images and refining labels through iterative pseudo labeling. Experiments across multiple medical imaging benchmarks demonstrate consistent gains in classification and segmentation performance, while Frechet Inception Distance analysis confirms the high quality of generated samples. These results highlight SSGNet as a practical strategy to mitigate annotation bottlenecks and improve robustness in medical image analysis.
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