PNEUMONIA DETECTION ON CHEST X-RAY USING RADIOMIC FEATURES AND CONTRASTIVE LEARNING.
PNEUMONIA DETECTION ON CHEST X-RAY USING RADIOMIC FEATURES AND CONTRASTIVE LEARNING.
复制标题
DOI：
10.1109/isbi48211.2021.9433853
复制
发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Peng, Yifan
中科院分区：
文献类型：
--
作者：
Han, Yan;Chen, Chongyan;Tewfik, Ahmed;Ding, Ying;Peng, Yifan
胸部x线检查因其无创性而成为最常见的医学诊断之一。胸部x光片的数量激增，但读取胸部x光片仍然是由放射科医生手动执行的，这造成了巨大的倦怠和延误。传统上，放射组学作为放射学的一个分支，可以从医学图像中提取大量的定量特征，在深度学习时代到来之前，放射组学展示了其促进医学影像诊断的潜力。随着深度学习的兴起，深度神经网络对胸部x线诊断的可解释性仍然不透明。在这项研究中，我们提出了一个新的框架，利用放射组学特征和对比学习来检测胸片中的肺炎。在RSNA肺炎检测挑战数据集上的实验表明，我们的模型取得了优于几个最先进模型的结果（f1得分为bb0 - 10%），并提高了模型的可解释性。
Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still has been manually performed by radiologists, which creates huge burnouts and delays. Traditionally, radiomics, as a subfield of radiology that can extract a large number of quantitative features from medical images, demonstrates its potential to facilitate medical imaging diagnosis before the deep learning era. With the rise of deep learning, the explainability of deep neural networks on chest X-ray diagnosis remains opaque. In this study, we proposed a novel framework that leverages radiomics features and contrastive learning to detect pneumonia in chest X-ray. Experiments on the RSNA Pneumonia Detection Challenge dataset show that our model achieves superior results to several state-of-the-art models (> 10% in F1-score) and increases the model’s interpretability.
登录
查看更多内容
影响因子：
11.2
作者：
van Griethuysen JJM;Fedorov A;Parmar C;Hosny A;Aucoin N;Narayan V;Beets-Tan RGH;Fillion-Robin JC;Pieper S;Aerts HJWL
通讯作者：
Aerts HJWL
影响因子：
6.1
作者：
Liang, Gaobo;Zheng, Lixin
通讯作者：
Zheng, Lixin
影响因子：
15.2
作者：
Tang, Yu-Xing;Tang, You-Bao;Summers, Ronald M.
通讯作者：
Summers, Ronald M.
影响因子：
4.6
作者：
Wang L;Lin ZQ;Wong A
通讯作者：
Wong A
影响因子：
5.6
作者：
Jaiswal, Amit Kumar;Tiwari, Prayag;Rodrigues, Joel J. P. C.
通讯作者：
Rodrigues, Joel J. P. C.