甲状腺疾病分类的多级学习:实践经验
Daniil Lysukhin1, Andrey Varlamov1, Boris Yakimov2
1Endocrinology Research Centre, Moscow, Russia.
Computers in biology and medicine
|November 29, 2024
概括
这项研究开发了一个人工智能 (AI) 模型,使用弱注释的真实世界数据从整个幻灯片图像 (WSI) 诊断甲状腺癌. 在患者层面上,AI模型成功地区分了良性和恶性甲状腺疾病.
科学领域:
- 数字病理学数字病理学
- 计算诊断是指计算机诊断.
- 医学中的人工智能.
背景情况:
- 甲状腺瘤的形态诊断是耗时的,需要专门的专业知识.
- 目前的人工智能 (AI) 研究通常依赖于精心策划的数据集,这些数据集的准备成本昂贵且复杂.
- 微弱注释的真实世界数据为诊断中人工智能模型开发提供了替代方案.
研究的目的:
- 开发和评估机器学习模型,使用弱注释的现实数据来诊断甲状腺瘤.
- 评估在患者级注释上训练的多级学习 (MIL) 模型的性能.
- 识别影响AI模型性能的潜在数据质量问题,并比较注释策略.
主要方法:
- 开发一个多级学习 (MIL) 模型,以1102名患者的患者级注释和5104个全幻灯片图像 (WSI) 来进行训练.
- 在没有选择性预处理的情况下利用"现实世界"数据.
- 在较小的数据集 (36个案例,91个WSI) 上,使用详细的幻灯片级和粗略的患者级注释来比较分类准确度.
主要成果:
- 在患者层面上,MIL模型实现了0.85 (±0.05) 的平均测试集F1-Score,以区分良性与恶性甲状腺疾病.
- 这代表了第一个在患者级数据上训练的AI模型,而没有先前的标签改进.
- 确定了数据质量陷,例如切除边缘染料,这可能导致模型过拟合.
- 详细的注释显著改善了较小数据集的分类性能.
结论:
- 人工智能模型可以有效地使用弱注释的现实数据来诊断甲状腺瘤,从而减少了对大量手动注释的需求.
- 患者级注释对于训练诊断AI模型是可行的,尽管必须仔细管理数据质量.
- 这些发现突出了AI在简化病理工作流程和提高诊断效率方面的潜力.
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