融合深度主动学习的医学图像半自动标注系统

Semi-automatic Labeling System for Medical Images Based on Deep Active Learning

目前深度学习在医学图像分析领域取得的良好表现大多取决于高质量带标注的数据集,但是医学图像由于其专业性和复杂性,数据集的标注工作往往需要耗费巨大的成本.本文针对这一问题设计了一种基于深度主动学习的半自动标注系统,该系统通过主动学习算法减少训练深度学习标注模型所需的标注样本数量,训练完成后的标注模型可以用于剩余数据集的标注工作.系统基于Web应用构建,无需安装且能跨平台访问,便于用户完成标注工作.

At present,the good performance of deep learning in medical image analysis mostly depends on high-quality labeled datasets.However,due to the professionalism and complexity of medical images,the labeling of datasets often requires huge costs.To tackle this problem,this study designs a semi-automatic labeling system based on deep active learning.This system reduces the number of labeled samples required for the training of the labeling model based on deep learning through the active learning algorithm,and the trained labeling model can be used for labeling the remaining dataset.The system is built on the basis of a Web application,which does not require installation and can be accessed across platforms.It is convenient for users to complete the labeling work.