General Pre-Trained Model for Magnetic Resonance Image Processing

磁共振图像处理通用预训练模型

• 批准号: 466989
• 负责人: Zhu Yi Cheng
• 金额: $ 1.27万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-12-01 至 2022-12-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=466989&lang=en
• 关键词: Deep Learning; Image Segmentation; Machine Learning; Medical Image Processing; Pretrainable Model; Transfer Learning; Unsupervised Training

Machine learning holds a great promise in biomedical research and clinical diagnosis. However, the state-of-the-art machine learning models depend on a large quantity of high-quality data, multiple orders of magnitude more than that typically available in

机器学习在生物医学研究和临床诊断方面有着巨大的前景。然而，最先进的机器学习模型依赖于大量高质量的数据，比人工智能中通常可用的数据多出多个数量级