Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation.
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation.
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DOI：
10.1016/j.compbiomed.2020.104037
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发表时间：
2020-11
影响因子：
7.7
通讯作者：
Ruan S
中科院分区：
文献类型：
--
作者：
Amyar A;Modzelewski R;Li H;Ruan S
本文介绍了一种利用胸部CT影像进行新冠肺炎肺炎筛查的自动分类分割工具。节段性病变有助于评估肺炎的严重程度和患者的随访。在这项工作中，我们提出了一种新的多任务深度学习模型，用于联合识别新冠肺炎患者和从胸部CT图像中分割新冠肺炎病变。对不同的数据集联合进行分割、分类和重构三个学习任务。我们的动机一方面是利用多个相关任务中包含的有用信息来提高分割和分类性能，另一方面是为了处理小数据问题，因为每个任务可以有一个相对较小的数据集。我们的体系结构由三个任务的解缠特征表示的通用编码器和分别用于重建、分割和分类的两个解码器和一个多层感知器组成。使用1369例患者的数据集对该模型进行了评估，并与其他图像分割技术进行了比较，其中包括449例新冠肺炎患者、425例正常患者、98例肺癌患者和397例不同病理类型的患者。实验结果表明，该方法分割效果良好，分割的骰子系数大于0.88，ROC曲线下面积大于97%。基于多任务深度学习的模型可以用于检测CT上的新冠肺炎病变。提出的模型可以通过利用多个相关任务中包含的有用信息来提高U-Net的技术水平。图像分割的骰子系数为88%，多类分类的准确率为94.67。所提出的模型可以作为辅助医生的支持工具。
This paper presents an automatic classification segmentation tool for helping screening COVID-19 pneumonia using chest CT imaging. The segmented lesions can help to assess the severity of pneumonia and follow-up the patients. In this work, we propose a new multitask deep learning model to jointly identify COVID-19 patient and segment COVID-19 lesion from chest CT images. Three learning tasks: segmentation, classification and reconstruction are jointly performed with different datasets. Our motivation is on the one hand to leverage useful information contained in multiple related tasks to improve both segmentation and classification performances, and on the other hand to deal with the problems of small data because each task can have a relatively small dataset. Our architecture is composed of a common encoder for disentangled feature representation with three tasks, and two decoders and a multi-layer perceptron for reconstruction, segmentation and classification respectively. The proposed model is evaluated and compared with other image segmentation techniques using a dataset of 1369 patients including 449 patients with COVID-19, 425 normal ones, 98 with lung cancer and 397 of different kinds of pathology. The obtained results show very encouraging performance of our method with a dice coefficient higher than 0.88 for the segmentation and an area under the ROC curve higher than 97% for the classification. Multitask deep learning based model can be used to detect COVID-19 lesions on CT scans. The proposed model can improve state of the art U-NET by leveraging useful information contained in multiple related tasks. Obtained a dice coefficient of 88% for image segmentation and an accuracy of 94.67 for multiclass classification. The proposed model can be used as a support tool to assist physicians.
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影响因子：
2.7
作者：
Loey, Mohamed;Smarandache, Florentin;Khalifa, Nour Eldeen M.
通讯作者：
Khalifa, Nour Eldeen M.
DOI：
10.1016/j.compmedimag.2016.12.002
发表时间：
2017-09-01
影响因子：
5.7
作者：
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通讯作者：
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影响因子：
5.1
作者：
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通讯作者：
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影响因子：
2.3
作者：
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通讯作者：
Shiran, Mohammad Bagher
影响因子：
7.5
作者：
Caruana, R
通讯作者：
Caruana, R