Semi supervised GAN for smart microscopy, fast and data efficient cell cycle classification

TL;DR

This paper presents a semi-supervised GAN framework for accurate, adaptable, and data-efficient cell cycle classification in microscopy, suitable for real-time smart microscopy systems.

Contribution

The authors introduce a semi-supervised GAN that effectively classifies cell cycle stages with limited labeled data, adaptable to various microscopy settings.

Findings

Achieved 93% accuracy with only 80 labeled images per class.

Effective even with class-imbalanced unlabelled data.

Framework is adaptable to different biological and microscopy applications.

Abstract

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 softwares 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.