Robust and Generalizable AI Models for Label-free Cellular Organelle Identification

光学显微镜是表征细胞内部器官（即细胞器）的重要研究工具。不幸的是，在不干扰细胞的情况下，通过实验标记这些结构以实现可视化通常是具有挑战性的。最近的研究表明，人工智能（AI）可以在显微镜图像中虚拟地标记细胞器。尽管人工智能技术有很大的潜力，但由于人工智能训练过程复杂，需要大量的训练数据，人工智能技术尚未得到广泛应用。为了克服这些障碍，该项目将开发两个开创性的人工智能图像翻译功能。首先，研究团队将实施一种迁移学习技术，允许人工智能模型将其先前的学习经验应用于新任务，从而减少对训练图像的需求。其次，研究团队将开发一种适应机制，以确保在不同成像条件下的准确和一致的预测，实现不同实验室之间的模型转移和共享。这种新的生物基础设施将为科学家提供一种有价值的工具，用于可视化活细胞内的细胞器和重要的生物过程。该项目将通过招募代表性不足的研究人员来支持教育和多样性。光学显微镜是表征细胞内部器官（即细胞器）的重要研究工具。不幸的是，在不干扰细胞的情况下，通过实验标记这些结构以实现可视化通常是具有挑战性的。最近的研究表明，人工智能（AI）可以在显微镜图像中虚拟地标记细胞器。尽管人工智能技术有很大的潜力，但由于人工智能训练过程复杂，需要大量的训练数据，人工智能技术尚未得到广泛应用。为了克服这些障碍，该项目将开发两个开创性的人工智能图像翻译功能。首先，研究团队将实施一种迁移学习技术，允许人工智能模型将其先前的学习经验应用于新任务，从而减少对训练图像的需求。其次，研究团队将开发一种适应机制，以确保在不同成像条件下的准确和一致的预测，实现不同实验室之间的模型转移和共享。这种新的生物基础设施将为科学家提供一种有价值的工具，用于可视化活细胞内的细胞器和重要的生物过程。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Light microscopy is an essential research tool for characterizing cell's internal organs, known as organelles. Unfortunately, it is often challenging to experimentally label these structures for visualization without substantially disturbing the cells. Recent studies have shown that artificial intelligence (AI) can virtually label organelles in microscope images. Despite the promising potential, AI techniques have not been widely used due to the complicated AI training processes and requirement of large training data. To overcome such obstacles, this project will develop two groundbreaking AI image translation features. First, the research team will implement a transfer learning technique that allows the AI model to apply its previous learning experiences to new tasks, reducing the need of training images. Second, the research team will develop an adaptation mechanism to ensure accurate and consistent predictions across different imaging conditions, enabling model transfer and sharing between different laboratories. This new bioinfrastructure will provide scientists with a valuable tool for visualizing organelles and important biological processes within living cells. The project will support education and diversity through the recruitment of underrepresented researchers.Light microscopy is an essential research tool for characterizing cell's internal organs, known as organelles. Unfortunately, it is often challenging to experimentally label these structures for visualization without substantially disturbing the cells. Recent studies have shown that artificial intelligence (AI) can virtually label organelles in microscope images. Despite the promising potential, AI techniques have not been widely used due to the complicated AI training processes and requirement of large training data. To overcome such obstacles, this project will develop two groundbreaking AI image translation features. First, the research team will implement a transfer learning technique that allows the AI model to apply its previous learning experiences to new tasks, reducing the need of training images. Second, the research team will develop an adaptation mechanism to ensure accurate and consistent predictions across different imaging conditions, enabling model transfer and sharing between different laboratories. This new bioinfrastructure will provide scientists with a valuable tool for visualizing organelles and important biological processes within living cells.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.