Efficient methods for the semi-supervised and weakly-supervised analysis of medical images

医学图像半监督和弱监督分析的有效方法

• 批准号: RGPIN-2018-05715
• 负责人: Desrosiers, Christian
• 金额: $ 1.68万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=754230
• 关键词: Efficient; methods; semi; supervised; weakly

Context Recently, artificial intelligence (AI) techniques have led to substantial improvements in performance for various problems of medical imaging, including image segmentation and reconstruction. However, these techniques often need large amounts of annotated data which are rarely available in real-life applications. This scarcity of annotated data impedes their ability of generalizing to new data, thereby limiting their adoption in clinical practice. In many medical applications, large amounts of unlabeled data are often obtainable, which could be exploited in a semi-supervised setting. The limited efficiency and scalability of current approaches is, however, a major obstacle to using this mass of unlabeled data.Research objectives and methodology This research aims at developing efficient methods for the segmentation and reconstruction of medical images, that can achieve state-of-the-art performance when annotated data are limited. Toward this goal, two complimentary axes of research will be explored. The first axis proposes to investigate novel approaches based on convolutional neural networks (CNNs) that exploit unlabeled data and low-cost annotations to improve performance when there are few labeled examples. This will be achieved by incorporating strong shape priors and constraints into the training process. The second research axis will investigate efficient and scalable methods based on distributed optimization to segment and reconstruct large images. These methods will accelerate processing times by decomposing complex optimization problems into smaller and easier sub-problems that can be solved in a distributed manner. The broader vision of this program is to make AI techniques more usable in clinical practice, by better exploiting available data and leveraging powerful optimization techniques.Contributions & impact This research proposes flexible and efficient methods to transfer problem-specific knowledge into data-driven methods like CNNs. By reducing the need for annotated data, these methods will alleviate the work of radiologists and other experts, saving both time and money. Improving the segmentation and reconstruction accuracy will also give clinicians a more reliable and complete information for diagnosis and treatment. Finally, increasing the scalability of current approaches will benefit to medical imaging and various other fields, where the volume and resolution of data is increasing each year.

背景最近，人工智能（AI）技术已经导致医疗成像的各种问题，包括图像分割和重建的性能有了实质性的改善。然而，这些技术往往需要大量的注释数据，这是很少在现实生活中的应用程序。这种注释数据的稀缺性阻碍了它们推广到新数据的能力，从而限制了它们在临床实践中的采用。在许多医学应用中，经常可以获得大量未标记的数据，这些数据可以在半监督环境中利用。有限的效率和可扩展性，目前的方法是，但是，使用这大量的未标记data.Research目标和方法本研究旨在开发有效的方法，用于分割和重建的医学图像，可以实现国家的最先进的性能时，注释数据是有限的。为了实现这一目标，将探讨两个相互补充的研究方向。第一个轴建议研究基于卷积神经网络（CNN）的新方法，这些方法利用未标记的数据和低成本注释来提高标记示例很少时的性能。这将通过将强形状先验和约束纳入训练过程来实现。第二个研究轴将研究基于分布式优化的高效和可扩展的方法来分割和重建大图像。这些方法将通过将复杂的优化问题分解为可以以分布式方式解决的较小且较容易的子问题来加快处理时间。该计划的更广泛的愿景是通过更好地利用可用数据和利用强大的优化技术，使人工智能技术在临床实践中更有用。贡献与影响本研究提出了灵活有效的方法，将特定问题的知识转化为数据驱动的方法，如CNN。通过减少对注释数据的需求，这些方法将减轻放射科医生和其他专家的工作，节省时间和金钱。提高分割和重建精度也将为临床医生的诊断和治疗提供更加可靠和完整的信息。最后，提高当前方法的可扩展性将有利于医学成像和其他各种领域，其中数据的数量和分辨率每年都在增加。