期刊
2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)
卷 -, 期 -, 页码 1216-1219出版社
IEEE
DOI: 10.1109/isbi.2019.8759573
关键词
Lungs X-ray; convolutional neural networks; deep learning; pathology prediction; data augmentation
资金
- Russian Science Foundation [18-71-10072]
- Russian Science Foundation [18-71-10072] Funding Source: Russian Science Foundation
Diagnosis of lung pathologies from CXRs is one of the main tasks in modern image-based diagnosis. Automation of lung pathology diagnosis is greatly facilitated by recent developments in deep learning-based clinical decision making. The performance of deep learning solutions has the tendency to improve with the growing number of training X-rays, which can be artificially increased by augmentation of training X-rays. Commonly, different augmentation approaches are greedily applied to the available training data without investigating the necessity and actual contribution of individual augmentation. Our work aims to an this gap in computerized lung pathology diagnosis and evaluate the contribution of different data augmentation approaches by leveraging the publicly available ChestX-ray14 dataset.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据