Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy

Bashir Kazimi, Karina Ruzaeva, Stefan Sandfeld; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 71-81

In this work we explore the potential of self-supervised learning with Generative Adversarial Networks (GANs) for electron microscopy datasets. We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks including semantic segmentation denoising noise & background removal and super-resolution. Experimentation with varying model complexities and receptive field sizes reveals the remarkable phenomenon that fine-tuned models of lower complexity consistently outperform more complex models with random weight initialization. We demonstrate the versatility of self-supervised pretraining across various downstream tasks in the context of electron microscopy allowing faster convergence and better performance. We conclude that self-supervised pretraining serves as a powerful catalyst being especially advantageous when limited annotated data are available and efficient scaling of computational cost is important.