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NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2021-05-02 , DOI: 10.1016/j.artmed.2021.102082
Wei Li 1 , Jinlin Chen 2 , Ping Chen 3 , Lequan Yu 4 , Xiaohui Cui 5 , Yiwei Li 6 , Fang Cheng 7 , Wen Ouyang 8
Affiliation  

During pandemics (e.g., COVID-19) physicians have to focus on diagnosing and treating patients, which often results in that only a limited amount of labeled CT images is available. Although recent semi-supervised learning algorithms may alleviate the problem of annotation scarcity, limited real-world CT images still cause those algorithms producing inaccurate detection results, especially in real-world COVID-19 cases. Existing models often cannot detect the small infected regions in COVID-19 CT images, such a challenge implicitly causes that many patients with minor symptoms are misdiagnosed and develop more severe symptoms, causing a higher mortality. In this paper, we propose a new method to address this challenge. Not only can we detect severe cases, but also detect minor symptoms using real-world COVID-19 CT images in which the source domain only includes limited labeled CT images but the target domain has a lot of unlabeled CT images. Specifically, we adopt Network-in-Network and Instance Normalization to build a new module (we term it NI module) and extract discriminative representations from CT images from both source and target domains. A domain classifier is utilized to implement infected region adaptation from source domain to target domain in an Adversarial Learning manner, and learns domain-invariant region proposal network (RPN) in the Faster R-CNN model. We call our model NIA-Network (Network-in-Network, Instance Normalization and Adversarial Learning), and conduct extensive experiments on two COVID-19 datasets to validate our approach. The experimental results show that our model can effectively detect infected regions with different sizes and achieve the highest diagnostic accuracy compared with existing SOTA methods.



中文翻译:

NIA-Network:致力于改进肺部 CT 感染检测以诊断 COVID-19

在大流行期间(例如,COVID-19),医生必须专注于诊断和治疗患者,这通常会导致只能使用有限数量的标记 CT 图像。尽管最近的半监督学习算法可以缓解注释稀缺的问题,但有限的现实世界 CT 图像仍然导致这些算法产生不准确的检测结果,特别是在现实世界的 COVID-19 病例中。现有的模型通常无法检测到 COVID-19 CT 图像中的小感染区域,这一挑战隐含地导致许多症状较轻的患者被误诊并出现更严重的症状,从而导致更高的死亡率。在本文中,我们提出了一种新方法来应对这一挑战。我们不仅可以检测严重病例,还可以使用真实世界的 COVID-19 CT 图像检测轻微症状,其中源域仅包括有限的标记 CT 图像,但目标域有大量未标记的 CT 图像。具体来说,我们采用网络中网络实例归一化来构建一个新模块(我们称之为 NI 模块),并从源域和目标域的 CT 图像中提取判别表示。利用域分类器以对抗学习的方式实现从源域到目标域的感染区域适应,并在Faster R-CNN模型中学习域不变区域提议网络(RPN)。我们将我们的模型称为NIA-Network网络中网络实例标准化对抗性学习),并在两个 COVID-19 数据集上进行了广泛的实验来验证我们的方法。实验结果表明,与现有的SOTA方法相比,我们的模型可以有效地检测不同大小的感染区域,并达到最高的诊断精度。

更新日期:2021-05-27
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