Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy

• URL: http://arxiv.org/abs/2402.18286v2
• Date: Thu, 18 Jul 2024 09:58:03 GMT
• Title: Self-Supervised Learning with Generative Adversarial Networks for Electron Microscopy
• Authors: Bashir Kazimi, Karina Ruzaeva, Stefan Sandfeld,
• Abstract summary: We show how self-supervised pretraining facilitates efficient fine-tuning for a spectrum of downstream tasks. We demonstrate the versatility of self-supervised pretraining across various downstream tasks in the context of electron microscopy.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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