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.

翻译：本文探索了从无标注电子显微镜数据集中进行自监督学习的潜力，迈出了构建该领域基础模型的一步。我们展示了自监督预训练如何促进多种下游任务的高效微调，包括语义分割、去噪、噪声与背景去除以及超分辨率。通过在不同模型复杂度和感受野大小下进行实验，揭示了低复杂度微调模型始终优于随机权重初始化的复杂模型的显著现象。我们证明了自监督预训练在电子显微镜背景下跨多种下游任务的普适性，能够实现更快的收敛和更优的性能。结论表明，自监督预训练作为一种强大的催化剂，在标注数据有限且计算成本高效扩展至关重要的情况下尤为有益。