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2019年底，新型冠状病毒COVID-19开始出现并迅速引起社会的关注，疫情的爆发给世界各地的卫生系统带来了巨大挑战。新冠病毒在传播的过程中也在不断进化，新的变种也在不断产生。特别地，当新冠病毒感染严重时，会侵入到肺部诱发肺炎。计算机断层扫描（CT）作为筛查肺炎的有效方法，可以提供可解释的视觉信息，用于肺炎的诊断和量化。

近年来，深度学习算法越来越多地被应用到医学领域，在很多疾病的诊断中都展现了强大的辅助诊断能力。因此，基于深度学习的相关方法，可以作为肺炎诊断的辅助手段，可以提供相关的可视化智能诊断结果，对于病程变化亦可起到有效监控的作用。

本文基于深度学习算法对由新冠病毒感染引起的肺炎CT影像进行多尺度自适应分割方法研究，主要的研究内容如下：

收集了相关的CT数据。搜寻了已有的公开的带标注的由新冠病毒感染引起的肺炎的CT影像数据并进行整理，分析相关的数据分布，并建立起统一的计算格式，为后续的深度学习算法提供数据支撑。

设计了适用于CT影像的多尺度分割模型。在基于编码器-解码器架构的多尺度分割基线网络基础上进行探索，设计了混合平衡注意力机制来提升模型对于病灶区域的认知学习能力，设计了上采样子网络来提升解码器部分的学习能力，通过与已有传统模型的对比以及对相关模块的分析验证了所设计模型整体的有效性，同时为后续的不同任务设置下的分割提供了改进之后的对比依据。

设计了适用于CT影像的领域自适应框架来完成不用来源的CT影像的领域适配。在基于图像翻译的无监督领域自适应迁移框架的基础上，引入了病变区域感知一致性约束，迫使深度学习模型关注领域无关的特征，从而在模型角度减少了私有领域与公开领域的领域分布差异，使得没有标注的私有数据的分割性能得到大幅提升，大大减少了对于私有数据的标注依赖，可视化的结果证明了迁移学习的有效性。

本文从CT影像数据出发，在数据处理、图像分割与迁移学习等方面进行探索，以提高基于深度学习的对由新冠病毒感染引起的肺炎进行诊断的算法的性能。同时，这也为分割技术在人工智能辅助诊断的其他应用提供了新思路。

At the end of 2019, the novel coronavirus COVID-19 began to emerge and quickly attracted the attention of the society. The outbreak of the epidemic has brought huge challenges to health systems around the world. COVID-19 is also evolving in the process of transmission, and new variants are also emerging. In particular, when COVID-19 is seriously infected, it will invade the lungs and cause pneumonia. As an effective method for screening pneumonia, computed tomography (CT) can provide interpretable visual information for the diagnosis and quantification of pneumonia.

In recent years, the deep learning algorithm has been more and more applied to the medical field, and has shown a strong auxiliary diagnosis ability in the diagnosis of many diseases. Therefore, the relevant methods based on deep learning can be used as an auxiliary means for diagnosis of pneumonia, can provide relevant visual intelligent diagnosis results, and can also play an effective role in monitoring the changes in the course of disease.

Based on the deep learning algorithm, this paper studies the adaptive segmentation and quantification method of the CT image of pneumonia caused by COVID-19 infection. The main research contents are as follows:

Relevant CT data were collected. The existing publicly labeled CT image data of pneumonia caused by COVID-19 infection were searched and sorted out, the relevant data distribution was analyzed, and a unified calculation format was established to provide data support for the subsequent in-depth learning algorithm.

A multi-scale segmentation model suitable for CT images is designed. Exploring a multi-scale segmentation baseline network based on encoder-decoder architecture, a hybrid balanced attention mechanism is designed to enhance the model's cognitive learning ability for lesion areas. An upsampling network is designed to enhance the decoder's learning ability. The overall effectiveness of the designed model was verified through comparison with existing traditional models and analysis of relevant modules. Meanwhile, the improved comparison basis is provided for the subsequent segmentation result analysis under different task settings.
A domain adaptation framework for CT images is designed to complete the domain adaptation of CT images from different sources. On the basis of the unsupervised Domain Adaptation adaptive migration framework based on image translation, the lesion-aware consistency constraint of lesion regions is introduced, forcing the deep learning model to focus on domain-invariant features, thus reducing the difference in domain distribution between private and public domains from the model perspective, greatly improving the segmentation performance of unlabeled private data, and greatly reducing the labeling dependency on private data. The visualization results prove the effectiveness of transfer learning.
Starting from CT image data, this paper explores how to improve the diagnostic ability of the algorithm based on deep learning for pneumonia caused by COVID-19 infection in terms of data processing, image segmentation and migration learning, and also provides new ideas for other applications that can use segmentation technology for artificial intelligence assisted diagnosis.