概括
基金会的模型正在通过从大量的成像和临床数据中学习,在放射学中推进人工智能 (AI). 本综述探讨了它们的技术方面,仅用于图像,胸部X射线和其他方式的应用,以及个性化放射学的未来方向.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 放射学中的人工智能 (AI) 正在向基础模型发展.
- 这些模型从大规模成像和多模式临床数据中学习通用表示.
研究的目的:
- 审查放射学基础模型的技术组成部分.
- 总结一下最近仅在图像,胸部X射线图像报告和其他图像报告模型方面的进展.
- 讨论评估策略,临床验证和未来的方向.
主要方法:
- 技术部件的审查:自我监督的编码器,融合模块,特定任务的解码器.
- 最近工作的分类:仅图像,胸部X射线图像报告和其他图像报告模型.
- 讨论评估策略:以视觉为中心,以语言为中心,以基准为基础的指标和临床验证.
主要成果:
- 基础模型通过在未标记的放射学扫描上进行自我监督学习,使得强大的转移学习与最小的注释.
- 联合视觉和文本嵌入使用大胸部X射线报告的身体.
- 多模式融合将体积图像与结构化报告或用于各种应用的临床元数据集成在一起.
结论:
- 基础模型代表了放射学AI的重大进步.
- 未来的方向包括多式联网整合和人-人工智能合作,以实现个性化放射学.
- 解决持续存在的挑战对于临床应用和验证至关重要.
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