Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework


Mosong Ma (Imperial College London), Tania Stathaki (Imperial College London), Michalis Lazarou (University of Surrey)
The 35th British Machine Vision Conference
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Abstract

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 semi–supervised 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 Fréchet 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. The publicly available source code can be found in https://github.com/sebastianotstan/SSGNet.git.

Citation

@inproceedings{Ma_2025_BMVC,
author    = {Mosong Ma and Tania Stathaki and Michalis Lazarou},
title     = {Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_646/paper.pdf}
}


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