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

背景近年来，人工智能(AI)技术在解决包括图像分割和重建在内的各种医学成像问题方面取得了实质性的进步。然而，这些技术通常需要大量的带注释的数据，而这些数据在实际应用中很少使用。这种注释数据的缺乏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，通常可以获得大量的未标记数据，这些数据可以在半监督环境中被利用。然而，当前方法有限的效率和可扩展性是使用这些大量未标记数据的主要障碍。 研究目标和方法本研究旨在开发有效的医学图像分割和重建方法，以便在标注数据有限的情况下获得最先进的性能。为了实现这一目标，将探索两个相辅相成的研究轴。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的注释来提高在有标记的例子很少的情况下的性能。这将通过在培训过程中纳入强大的形状先验条件和约束来实现。第二个研究轴将研究基于分布式优化的高效和可伸缩的大图像分割和重建方法。这些方法将复杂的优化问题分解成更小、更容易的子问题，以分布式方式解决，从而加快处理时间。该计划的更广泛的愿景是通过更好地利用可用的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。 贡献与影响这项研究提出了灵活而有效的方法，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。提高分割和重建的准确性也将为临床医生提供更可靠、更完整的诊断和治疗信息。最后，提高现有方法的可扩展性将有利于医学成像和其他各种领域，这些领域的数据量和分辨率每年都在增加。

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.