一个基于MRI的综合诊断框架,用于质瘤,具有不完整的成像序列和不完美的注释
Pengfei Song1, Chao Shen2,3,4,5, Zhen Fan2,3,4,5
1College of Biomedical Engineering, Fudan University, Shanghai, China.
NPJ precision oncology
|October 23, 2025
概括
这项研究介绍了SSL-MISS-Net,这是一种新的AI框架,通过有效处理不完整的成像和分子数据来改进质瘤诊断. 该方法显著提高了诊断准确度,使得更多的临床数据可用于AI开发.
科学领域:
- 人工智能在医学中的应用
- 神经瘤学神经瘤学
- 医学成像分析 医学成像分析
背景情况:
- 质瘤诊断依赖于将组织病理与分子特征 (IDH,1p/19q) 整合在一起,但由于各种成像协议和分子测试,多中心研究面临数据异质性.
- 不一致的数据,包括不完整的成像和缺失的注释,阻碍了强大的AI诊断工具的开发.
研究的目的:
- 提出SSL-MISS-Net,一个统一的框架,旨在同时解决输入端模式不完整性和输出端注释缺陷的质瘤数据.
- 减少对完整,完美注释的数据集的依赖,以训练神经瘤学中的AI模型.
主要方法:
- 开发了SSL-MISS-Net (自主监督学习与MIssing-label编码和语义合成),一个使用跨模式自主监督学习用于模式不完整性的框架.
- 整合了一个缺失标签的协同策略来处理注释缺陷,使得不完美的临床数据的使用.
主要成果:
- 与低于最佳的方法相比,SSL-MISS-Net在综合性质瘤诊断方面实现了4% (验证) 和10% (测试) 的显著准确性增长.
- 该框架将临床可用数据的数量扩大了256%,并超过了在完整数据上训练的最先进方法.
- 在6个内部和3个公共数据集的多中心队列中 (N=2238) 证明了对数据缺陷的特殊弹性.
结论:
- SSL-MISS-Net是第一个共同解决质瘤数据中模式不完整性和缺失标签的框架,释放不完美的临床数据集的潜力.
- 拟议的方法在神经瘤学AI诊断中显示出强大的临床可翻译性和强大的性能,克服了常见的数据挑战.
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