一句话：提出 TauMed，基于变异规则与领域语义增强医学图像，提高深度学习诊断模型测试数据质量与分类准确率。
深度学习在医学诊断中取得进展，但受数据标准化和隐私限制，医学图像数据获取与共享困难，导致部分智能诊断模型精度不足；同时，数据数量不足和低质量可能引发严重医疗事故。常用数据增强依赖变异关系，但普通变异方法难以保证医学图像质量。为此，作者结合医学图像特点提出 TauMed，基于一系列变异规则和领域语义对医学数据集进行增强，生成充足且高质量的图像。实验选择 ResNet-50 在增强数据集上训练测试，并与两个主流变异工具比较。结果表明，TauMed 能有效提升模型分类准确率，且增强图像质量高于这两个工具。
Deep learning has made great progress in medical diagnosis. However, due to data standardization and privacy restriction, the acquisition and sharing of medical image data have been hindered, leading to the unacceptable accuracy of some intelligent medical diagnosis models. Another concern is data quality. If insufficient quantity and low-quality data are used for training and testing medical diagnosis models, it may cause serious medical accidents. We always use data augmentation to deal with it, and one of the most representative ways is through mutation relation. However, although common mutation methods can increase the amount of medical data, the quality of the image cannot be guaranteed due to the particularity of medical image. Therefore, combined with the characteristics of medical images, we propose TauMed, which implements augmentation techniques based on a series of mutation rules and domain semantics on medical datasets to generate sufficient and high-quality images. Moreover, we chose the ResNet-50 model to experiment with the augmented dataset and compared the results with two main popular mutation tools. The experimental result indicates that TauMed can improve the classification accuracy of the model effectively, and the quality of augmented images is higher than the other two tools. Its video is at https://www.youtube.com/watch?v=O8W8I7U_eqk and TauMed can be used at http://121.196.124.158:9500/.
DOI 原文 ·
@inproceedings{paperbot6645,
title = {TauMed: test augmentation of deep learning in medical diagnosis},
author = {Yunhan Hou and Jiawei Liu and Daiwei Wang and Jiawei He and Chunrong Fang and Zhenyu Chen},
booktitle = {ISSTA},
year = {2021},
doi = {10.1145/3460319.3469080}
}