knee_OA_staging

上传者: 42110533 | 上传时间: 2022-10-15 15:52:24 | 文件大小: 43.42MB | 文件类型: ZIP
使用更快的R-CNN模型从X射线图像自动量化膝盖骨关节炎的严重程度 作者:Suhas Suresha,Eni Halilaj和ŁukaszKidziński 介绍 使用Python实施的此代码仓库可用于使用训练有素的更快的区域卷积神经网络(R-CNN)模型从X射线图像自动量化膝关节骨关节炎的严重程度(使用Kellgren&Lawrence评分系统)。 该模型在由斯坦福大学动员中心提供的骨关节炎倡议(OAI)数据集获得的包括膝盖(包括左腿和右腿)的4214幅X射线图像组成的数据集上进行了训练。 执照 项目“膝盖OA阶段”是根据MIT许可证发布的(有关详细信息,请参阅LICENSE文件)。 方法 我们培养更快的区域卷积神经网络(更快R-CNN)与我们的数据集中,以实现以下两个目标:基于KL从X射线图像1.提取物中的膝关节区域和2分类所提取的膝关节区域分数。 更快的R-CNN可以同时实现这两个目

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