Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911196124282880 |
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| author | Ma, Mosong Stathaki, Tania Lazarou, Michalis |
| author_facet | Ma, Mosong Stathaki, Tania Lazarou, Michalis |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06123 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework Ma, Mosong Stathaki, Tania Lazarou, Michalis Computer Vision and Pattern Recognition 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. |
| title | Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.06123 |