Computational Methods for Medical Image Interpretation

医学图像解释的计算方法

• 批准号: RGPIN-2015-06795
• 负责人: Hamarneh, Ghassan
• 金额: $ 3.64万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=591295
• 关键词: Computational; Methods; Medical; Image; Interpretation

Digital image data is being generated and collected at an astounding rate for numerous applications such as social media communication, defence and security, industrial manufacturing, agriculture and environment, science and biology, healthcare and medicine, etc. Today, static and dynamic images (video) constitute about 80% of all big data. This big ‘visual’ data hides within it extraordinary amount of information that is critical for providing new insights, building predictive models, guiding decision making, performing search and curation, etc. This is necessitating the design of new computational tools for image analysis that no longer is possible to carry out manually via visual inspection.

数字图像数据正在以惊人的速度生成和收集，用于社交媒体通信、国防和安全、工业制造、农业和环境、科学和生物、医疗保健和医学等众多应用。如今，静态和动态图像(视频)约占所有大数据的80%。这种巨大的“视觉”数据隐藏着大量的信息，这些信息对于提供新的见解、建立预测模型、指导决策、执行搜索和管理等至关重要。这就需要为图像分析设计新的计算工具，而不再可能通过视觉检查手动执行。