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Generating Synthetic Data for Medical Imaging.
Radiology ( IF 12.1 ) Pub Date : 2024-09-01 , DOI: 10.1148/radiol.232471
Lennart R Koetzier 1 , Jie Wu 1 , Domenico Mastrodicasa 1 , Aline Lutz 1 , Matthew Chung 1 , W Adam Koszek 1 , Jayanth Pratap 1 , Akshay S Chaudhari 1 , Pranav Rajpurkar 1 , Matthew P Lungren 1 , Martin J Willemink 1
Affiliation  

Artificial intelligence (AI) models for medical imaging tasks, such as classification or segmentation, require large and diverse datasets of images. However, due to privacy and ethical issues, as well as data sharing infrastructure barriers, these datasets are scarce and difficult to assemble. Synthetic medical imaging data generated by AI from existing data could address this challenge by augmenting and anonymizing real imaging data. In addition, synthetic data enable new applications, including modality translation, contrast synthesis, and professional training for radiologists. However, the use of synthetic data also poses technical and ethical challenges. These challenges include ensuring the realism and diversity of the synthesized images while keeping data unidentifiable, evaluating the performance and generalizability of models trained on synthetic data, and high computational costs. Since existing regulations are not sufficient to guarantee the safe and ethical use of synthetic images, it becomes evident that updated laws and more rigorous oversight are needed. Regulatory bodies, physicians, and AI developers should collaborate to develop, maintain, and continually refine best practices for synthetic data. This review aims to provide an overview of the current knowledge of synthetic data in medical imaging and highlights current key challenges in the field to guide future research and development.

中文翻译:


生成医学成像的综合数据。



用于医学成像任务(例如分类或分割)的人工智能 (AI) 模型需要大量且多样化的图像数据集。然而,由于隐私和道德问题以及数据共享基础设施障碍,这些数据集稀缺且难以组装。人工智能根据现有数据生成的合成医学成像数据可以通过增强和匿名化真实成像数据来应对这一挑战。此外,合成数据还支持新的应用,包括模态转换、对比合成和放射科医生的专业培训。然而,合成数据的使用也带来了技术和道德挑战。这些挑战包括确保合成图像的真实性和多样性,同时保持数据不可识别,评估在合成数据上训练的模型的性能和通用性,以及高计算成本。由于现有法规不足以保证合成图像的安全和道德使用,因此显然需要更新法律和更严格的监督。监管机构、医生和人工智能开发人员应该合作开发、维护和不断完善合成数据的最佳实践。本综述旨在概述医学成像合成数据的当前知识,并强调该领域当前的关键挑战,以指导未来的研究和开发。
更新日期:2024-09-01
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