Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917550048149504 |
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| author | Manick, Rajeev Habouz, Youssef El Guillout, Maëlle Martin, Celia Bonnet, Julia Ruel, Louis Pastezeur, Sylvain Chanteux, Olivier Bouchareb, Otmane Tramier, Marc Pécréaux, Jacques |
| author_facet | Manick, Rajeev Habouz, Youssef El Guillout, Maëlle Martin, Celia Bonnet, Julia Ruel, Louis Pastezeur, Sylvain Chanteux, Olivier Bouchareb, Otmane Tramier, Marc Pécréaux, Jacques |
| contents | Modern optical microscopes are fully motorised; however, transforming them into truly smart systems requires real-time adjustment of acquisition settings in response to detected objects and dynamic biological events. At the core are classification algorithms that commonly depend on customised software and are generally designed for narrowly-defined biological applications. In addition, they often require substantial annotated datasets for effective training. We introduce a semi-supervised generative adversarial network (SGAN) for robust cell-cycle stage classification under low-resource conditions, adaptable to diverse cellular structures. The framework combines unlabelled microscopy images with synthetically generated samples to mitigate limited annotation, while preserving stable performance even when the unlabelled subset is class-imbalanced. Tested on the Mitocheck dataset, which features five mitosis classes, the model achieved $93 \pm 2\%$ accuracy using only 80 labelled per class and 600 unlabelled images. The proposed algorithm is generic and can be readily adapted to new labeling schemes, classification targets, cell lines, or microscopy modalities through transfer learning. SGAN is well suited for integration into automated microscopes, enabling efficient and adaptable image analysis across diverse biological and microscopy applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20615 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification Manick, Rajeev Habouz, Youssef El Guillout, Maëlle Martin, Celia Bonnet, Julia Ruel, Louis Pastezeur, Sylvain Chanteux, Olivier Bouchareb, Otmane Tramier, Marc Pécréaux, Jacques Quantitative Methods I.2.10; I.4.8 Modern optical microscopes are fully motorised; however, transforming them into truly smart systems requires real-time adjustment of acquisition settings in response to detected objects and dynamic biological events. At the core are classification algorithms that commonly depend on customised software and are generally designed for narrowly-defined biological applications. In addition, they often require substantial annotated datasets for effective training. We introduce a semi-supervised generative adversarial network (SGAN) for robust cell-cycle stage classification under low-resource conditions, adaptable to diverse cellular structures. The framework combines unlabelled microscopy images with synthetically generated samples to mitigate limited annotation, while preserving stable performance even when the unlabelled subset is class-imbalanced. Tested on the Mitocheck dataset, which features five mitosis classes, the model achieved $93 \pm 2\%$ accuracy using only 80 labelled per class and 600 unlabelled images. The proposed algorithm is generic and can be readily adapted to new labeling schemes, classification targets, cell lines, or microscopy modalities through transfer learning. SGAN is well suited for integration into automated microscopes, enabling efficient and adaptable image analysis across diverse biological and microscopy applications. |
| title | Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification |
| topic | Quantitative Methods I.2.10; I.4.8 |
| url | https://arxiv.org/abs/2604.20615 |