可扩展的临床注释与位置证据 (SCALE)
Joeran S Bosma1, Luc Builtjes2, Anindo Saha3
1Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Health & Information Technology, Ziekenhuisgroep Twente, Almelo, The Netherlands; Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.
Computers in biology and medicine
|November 22, 2025
概括
本研究介绍了SCALE,这是一种用于创建大规模注释医疗数据集的自动化方法. 用SCALE注释训练的AI模型在MRI上检测前列腺癌方面表现出卓越的表现.
科学领域:
- 医学成像人工智能 医学成像人工智能
- 机器学习在放射学中的应用
- 前列腺癌的诊断方法 前列腺癌的诊断方法
背景情况:
- 医学成像的深度学习需要大量的注释数据集,这些数据很难获得.
- 全球短缺的放射科医生需要有效的AI开发医疗图像分析.
- 自动注释方法对于扩大医疗保健中的AI发展至关重要.
研究的目的:
- 引入SCALE (可扩展的临床注释与位置证据),这是一个完全自动化的方法,用于生成语音级注释.
- 开发和训练一个优化的AI算法,使用大规模的数据集,用SCALE进行注释.
- 用SCALE注释训练的人工智能模型的性能与其他前列腺癌检测方法在MRI上的性能进行评估.
主要方法:
- 开发了SCALE,一种利用医疗报告,活检坐标或解剖部门的位置先验进行自动注释的方法.
- 用SCALE和基于计数的弱半监督学习 (CWSSL) 方法对17,896个案例的大数据集进行了注释.
- 在由SCALE和CWSSL生成的数据集上训练和评估一个优化的AI算法,比较性能与监督学习和PI-CAI集体AI系统.
主要成果:
- 在SCALE注释数据上训练的AI模型在接收器操作特征曲线 (AUC) 下实现了0.856.6的案例级区域.
- 这种表现优于受监督学习训练的模型 (AUC +0.012,p=0.02) 和可比于CWSSL (AUC +0.007,p=0.12).
- 经过SCALE训练的模型也比PI-CAI整体AI系统略有优势 (AUC +0.006).
结论:
- 使用SCALE的自动化,位置导向注释使人工智能的可扩展开发能够在MRI上进行临床显著的前列腺癌检测.
- 该方法超越了以前的注释和AI培训方法,促进了人工智能工具的更广泛的临床部署.
- 这项工作展示了自动注释的潜力,以解决医疗AI研究和开发中的数据限制.
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