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

用于无标记细胞器识别的稳健且可推广的人工智能模型

• 批准号: 2325121
• 负责人: Neil Lin
• 金额: $ 67.32万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2325121&HistoricalAwards=false
• 关键词: Robust; Generalizable; AI; Models; Label

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.

光学显微镜是表征细胞内部器官（称为细胞器）的必不可少的研究工具。不幸的是，实验将这些结构标记以进行可视化而不实质性干扰细胞通常是具有挑战性的。最近的研究表明，人工智能（AI）实际上可以在显微镜图像中标记细胞器。尽管有希望的潜力，但由于AI训练过程复杂和大型培训数据的要求，AI技术并未得到广泛使用。为了克服此类障碍，该项目将开发两个开创性的AI图像翻译功能。首先，研究团队将实施转移学习技术，该技术使AI模型可以将其先前的学习经验应用于新任务，从而减少了培训图像的需求。其次，研究团队将开发一种适应机制，以确保在不同的成像条件上进行准确，一致的预测，从而实现模型转移并在不同实验室之间共享。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，可视化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（称为细胞器）的重要研究工具。不幸的是，实验将这些结构标记以进行可视化而不实质性干扰细胞通常是具有挑战性的。最近的研究表明，人工智能（AI）实际上可以在显微镜图像中标记细胞器。尽管有希望的潜力，但由于AI训练过程复杂和大型培训数据的要求，AI技术并未得到广泛使用。为了克服此类障碍，该项目将开发两个开创性的AI图像翻译功能。首先，研究团队将实施转移学习技术，该技术使AI模型可以将其先前的学习经验应用于新任务，从而减少了培训图像的需求。其次，研究团队将开发一种适应机制，以确保在不同的成像条件上进行准确，一致的预测，从而实现模型转移并在不同实验室之间共享。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，可在活细胞中可视化细胞器和重要的生物学过程。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估评估标准的评估值得支持的。