医学影像中的知识升华与师生学习:综合概述、关键作用与未来方向

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiang Li, Like Li, Minglei Li, Pengfei Yan, Ting Feng, Hao Luo, Yong Zhao, Shen Yin
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引用次数: 0

摘要

知识蒸馏(Knowledge Distillation, KD)是一种将知识从复杂模型转移到简化模型的技术。它在自然语言处理和计算机视觉中得到了广泛的应用,并取得了先进的成果。近年来,KD在医学图像分析中的研究发展迅速。知识的定义通过与医学领域的结合得到了进一步的扩展,其作用也不局限于简化模型。本文就KD在医学影像领域的发展和应用作一综述。具体来说,我们首先介绍了基本原理,解释了知识的定义和经典的师生网络框架。然后介绍了医学图像分类、分割、检测、重建、配准、放射学报告生成、隐私保护等应用场景的研究进展。特别要介绍的是基于KD角色的应用场景。总结了KD技术在医学图像分析中的八个主要作用,包括模型压缩、半监督方法、弱监督方法、类平衡等。分析了这些角色在各种应用场景下的性能。最后,我们讨论了该领域面临的挑战,并提出了可能的解决方案。KD在医学影像领域仍处于快速发展阶段,我们给出了五个潜在的发展方向和研究热点。该调查的综合文献列表可在https://github.com/XiangQA-Q/KD-in-MIA上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge distillation and teacher–student learning in medical imaging: Comprehensive overview, pivotal role, and future directions
Knowledge Distillation (KD) is a technique to transfer the knowledge from a complex model to a simplified model. It has been widely used in natural language processing and computer vision and has achieved advanced results. Recently, the research of KD in medical image analysis has grown rapidly. The definition of knowledge has been further expanded by combining with the medical field, and its role is not limited to simplifying the model. This paper attempts to comprehensively review the development and application of KD in the medical imaging field. Specifically, we first introduce the basic principles, explain the definition of knowledge and the classical teacher–student network framework. Then, the research progress in medical image classification, segmentation, detection, reconstruction, registration, radiology report generation, privacy protection and other application scenarios is presented. In particular, the introduction of application scenarios is based on the role of KD. We summarize eight main roles of KD techniques in medical image analysis, including model compression, semi-supervised method, weakly supervised method, class balancing, etc. The performance of these roles in all application scenarios is analyzed. Finally, we discuss the challenges in this field and propose potential solutions. KD is still in a rapid development stage in the medical imaging field, we give five potential development directions and research hotspots. A comprehensive literature list of this survey is available at https://github.com/XiangQA-Q/KD-in-MIA.
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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