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首页> 外文期刊>Proceedings of the IEEE >Model-Based and Data-Driven Strategies in Medical Image Computing
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Model-Based and Data-Driven Strategies in Medical Image Computing

机译：医学图像计算中基于模型和数据驱动的策略

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Model-based approaches for image reconstruction, analysis, and interpretation have made significant progress over the past decades. Many of these approaches are based on either mathematical, physical, or biological models. A challenge for these approaches is the modeling of the underlying processes (e.g., the physics of image acquisition or the patho-physiology of a disease) with appropriate levels of detail and realism. With the availability of large amounts of imaging data and machine learning (in particular deep learning) techniques, data-driven approaches have become more widespread for use in different tasks in reconstruction, analysis, and interpretation. These approaches learn statistical models directly from labeled or unlabeled image data and have been shown to be very powerful for extracting clinically useful information from medical imaging. While these data-driven approaches often outperform traditional model-based approaches, their clinical deployment often poses challenges in terms of robustness, generalization ability, and interpretability. In this article, we discuss what developments have motivated the shift from model-based approaches toward data-driven strategies and what potential problems are associated with the move toward purely data-driven approaches, in particular deep learning. We also discuss some of the open challenges for data-driven approaches, e.g., generalization to new unseen data (e.g., transfer learning), robustness to adversarial attacks, and interpretability. Finally, we conclude with a discussion on how these approaches may lead to the development of more closely coupled imaging pipelines that are optimized in an end-to-end fashion.
机译：在过去的几十年中，基于模型的图像重建，分析和解释方法取得了重大进展。这些方法很多都基于数学，物理或生物学模型。这些方法的挑战是对基础过程（例如，图像采集的物理过程或疾病的病理生理学）进行建模，并具有适当的细节和逼真度。随着大量成像数据和机器学习（尤其是深度学习）技术的可用性，数据驱动的方法已越来越广泛地用于重建，分析和解释的不同任务。这些方法可直接从标记或未标记的图像数据中学习统计模型，并且已证明对于从医学成像中提取临床有用信息非常有力。尽管这些数据驱动的方法通常要优于传统的基于模型的方法，但它们的临床部署通常会在鲁棒性，泛化能力和可解释性方面提出挑战。在本文中，我们讨论了哪些发展推动了从基于模型的方法向数据驱动的策略的转变，以及哪些潜在的问题与向纯数据驱动的方法（尤其是深度学习）的迁移相关。我们还将讨论数据驱动方法所面临的一些开放挑战，例如，对新的未见数据进行通用化（例如，转移学习），对抗攻击的鲁棒性和可解释性。最后，我们以讨论这些方法如何导致以端到端的方式优化的更紧密耦合的成像管道的开发作为结束。

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