将物理原理融入AI，提升医疗影像模型的可信与稳健性
Physical foundations for trustworthy medical imaging: a review for artificial intelligence researchers
- 结合医学成像物理机制设计AI算法
- 在数据稀缺时仍保持高可靠性
- 适合刚入行或想提升模型可信度的研究者
近年来，深度学习与算力进步推动了医疗影像中人工智能的迅猛发展，涵盖各类成像模态。每种技术因其物理特性而具有独特表现。然而，许多人工智能研究者对成像物理基础理解不足，制约了模型潜力的发挥。将物理知识融入算法可显著提升其在数据有限场景下的可信度与鲁棒性。本文综述了医学影像中的物理基础及其对生成模型和重建算法最新进展的影响，并探讨了基于物理约束的机器学习模型，通过引入物理规律增强特征学习能力。
原文摘要 · Abstract (English)
Artificial intelligence in medical imaging has seen unprecedented growth in the last years, due to rapid advances in deep learning and computing resources. Applications cover the full range of existing medical imaging modalities, with unique characteristics driven by the physics of each technique. Yet, artificial intelligence professionals entering the field, and even experienced developers, often lack a comprehensive understanding of the physical principles underlying medical image acquisition, which hinders their ability to fully leverage its potential. The integration of physics knowledge into artificial intelligence algorithms enhances their trustworthiness and robustness in medical imaging, especially in scenarios with limited data availability. In this work, we review the fundamentals of physics in medical images and their impact on the latest advances in artificial intelligence, particularly, in generative models and reconstruction algorithms. Finally, we explore the integration of physics knowledge into physics-inspired machine learning models, which leverage physics-based constraints to enhance the learning of medical imaging features.
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