More efficient deep learning for medical image analysis

更高效的深度学习用于医学图像分析

• 批准号: 2644381
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2644381
• 关键词: More; efficient; deep; learning; medical

Artificial intelligence in the form of deep learning, for instance using convolutional neural networks, has made a huge impact on medical image analysis. It dominates conference and journal publications and has demonstrated state-of-the-art performance in many benchmarks and applications, outperforming human observers in some situations. But, despite this, adoption of these approaches in routine clinical practice has been very slow. One reason for this is that deep learning models are inefficient and expensive to train, often requiring tens or hundreds of thousands of expertly labelled training images, and many days training on high-end GPU hardware. For medical applications the requirement for so much expertly labelled data is a key challenge. After all, a radiologist (a doctor specially trained to interpret medical images) is able to learn new tasks using a far smaller set of training images. This project will investigate approaches to improve the efficiency of training deep learning models, reducing the size and/or level of detail of the required training set whilst maintaining diagnostic accuracy. This would enable more clinical applications to be developed sooner, driving improved healthcare. In addition, more efficient models may also enable applications to run on lower-end hardware, giving developing countries access to the latest advanced clinical applications.Novelty of Project-The extravagant data and power requirements of current state-of-the-art deep learning algorithms that limit their rapid deployment and wide use are well recognized; reducing these requirements remains a hot research topic.

以深度学习为形式的人工智能，例如使用卷积神经网络，对医学图像分析产生了巨大的影响。它在会议和期刊出版物中占据主导地位，并在许多基准测试和应用程序中表现出最先进的性能，在某些情况下优于人类观察者。但是，尽管如此，这些方法在常规临床实践中的采用仍然非常缓慢。其中一个原因是，深度学习模型的训练效率低下且成本高昂，通常需要数万或数十万张经过专业标记的训练图像，并在高端GPU硬件上进行数天的训练。对于医疗应用来说，需要如此多的专业标记数据是一个关键挑战。毕竟，放射科医生（受过专门训练的医生，可以解读医学图像）能够使用更小的训练图像集来学习新任务。该项目将研究提高深度学习模型训练效率的方法，减少所需训练集的大小和/或细节水平，同时保持诊断准确性。这将使更多的临床应用程序能够更快地开发，推动医疗保健的改善。此外，更高效的模型也可能使应用程序能够在低端硬件上运行，从而使发展中国家能够获得最新的先进临床应用。项目的新奇性-目前最先进的深度学习算法对数据和功耗的过度要求限制了其快速部署和广泛使用，这一点已得到充分认可;降低这些要求仍然是一个热门的研究课题。