Self-Supervised Learning in Electron Microscopy: Towards a Foundation
  Model for Advanced Image Analysis
Bashir Kazimi Karina Ruzaeva Stefan Sandfeld
In this work, we explore the potential of self-supervised learning from
unlabeled electron microscopy datasets, taking a step toward building a
foundation model in this field. 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 are important.