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使用物体检测深度学习算法匿名化放射图

Bardia Khosravi¹, John P Mickley¹, Pouria Rouzrokh¹

¹From the Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopedic Surgery (B.K., J.P.M., P.R., M.J.T., A.N.L., C.C.W.), Radiology Informatics Laboratory, Department of Radiology (B.K., P.R., B.J.E.), Department of Orthopedic Surgery (M.J.T., A.N.L., C.C.W.), and Department of Clinical Anatomy (C.C.W.), Mayo Clinic, 200 1st St SW, Rochester, MN 55905.

Radiology. Artificial intelligence

|December 11, 2023

概括

一个深度学习算法有效地从医疗图像中删除放射性标记,从而实现非识别数据共享. 这种监督学习方法确保了患者的隐私,同时保留了诸如横向标记器之类的有用信息.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

医疗图像中的放射标记包含受保护的健康信息.
删除这些数据对于非身份化数据共享和研究至关重要.
现有的方法可能不够有效或足够准确,无法进行全面的非识别.

研究的目的:

开发和验证用于定位和删除放射标记的深度学习算法.
为了使医疗放射图片的安全无标识数据共享.
在内部和外部数据集上评估算法的准确性和有效性.

主要方法:

2000张关节和骨盆X射线图的注释,用于训练一个物体检测模型.
使用涉及标记物定位和表征的双通道方法.
使用卷积神经网络 (CNN) 的监督学习和转移学习.
在外部数据集上验证有微调的胸部X射线图.

主要成果:

在内部测试组上,在精度回忆曲线下的面积达到0.96.
对于内部数据上的横向标记,已证明100%的非识别精度和93%的保留精度.
外部验证显示96%的非识别准确度,在微调后改进到99.6%.
该算法有效地删除了识别信息,同时保留了临床相关的标记.

结论:

开发的深度学习算法在去识别放射图像方面非常有效.
双通道方法提供了一个强大的解决方案,可以从医疗数据中删除受保护的健康信息.
这项技术为研究和临床进展提供了安全的数据共享.

关键词:

传统的X光学X光学卷积神经网络 (CNN) 是一种神经网络.实验调查实验调查骨架与轴的关系监督学习监督学习胸部胸部胸部胸部转移学习转移学习

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