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AD HOC MODEL BUILDING AND MACHINE LEARNING SERVICES FOR RADIOLOGY QUALITY DASHBOARD

机译:Ad Hoc模型建筑和机器学习服务,用于放射线质量仪表板

摘要

A method (100) of generating and using one or more radiology analysis tools comprising: providing a labeling user interface (28, 40) on the workstation via which a user creates a labeled dataset by defining label types; receiving a user selection of a desired output as at least one of the defined label types; identifying a proposed machine learning (ML) model based on the defined label types and the desired output; providing one or more GUI dialogs (40) presenting the proposed ML model and allowing the user to generate a user-designed proposed ML model (38) from the proposed ML model; training the user-designed ML model using training data comprising at least a portion of the labeled dataset, thereby generating a trained ML model (44); and deploying the trained ML model for an analysis process applied to at least a portion of radiology images and/or radiology reports in.
机译:生成和使用一个或多个放射学分析工具的方法(100),包括:在工作站上提供标签用户界面(28,40),通过定义标签类型来通过该工作站创建标记的数据集; 接收用户选择所需输出作为所定义的标签类型中的至少一个; 基于定义的标签类型和所需输出识别所提出的机器学习(ML)模型; 提供一个或多个GUI对话框(40)呈现所提出的ML模型,并允许用户从所提出的ML模型生成用户设计的建议的ML模型(38); 使用包括标记数据集的至少一部分的训练数据训练用户设计的ML模型,从而产生训练的ML型号(44); 并将训练的ML模型部署用于应用于至少一部分放射学图像和/或放射学报告的分析过程。

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