用GAN和滑窗提升CT图像质量，准确诊断新冠肺部感染
Deep-Learning-Assisted Highly-Accurate COVID-19 Diagnosis on Lung Computed Tomography Images
- 通过GAN与滑窗技术优化CT图像质量
- 在基准数据集上实现0.983的马修相关系数
- 适合医学影像分析与新冠筛查研究者
COVID-19是一种严重急性病毒性疾病，可导致肺泡区域炎症，引发肺部积液和呼吸困难。利用CT扫描进行新冠诊断，有助于辅助RT-PCR检测并评估病情严重程度。本文提出一种基于生成对抗网络（GAN）与滑窗策略的新数据质量控制流程，以提升CT图像质量。同时引入类敏感损失函数，包括标签分布感知损失（LDAM Loss）与类别平衡损失（CB Loss），解决数据集中的长尾问题。所提模型在基准测试数据集上达到超过0.983的马修相关系数（MCC），显著提升诊断准确性。
原文摘要 · Abstract (English)
COVID-19 is a severe and acute viral disease that can cause symptoms consistent with pneumonia in which inflammation is caused in the alveolous regions of the lungs leading to a build-up of fluid and breathing difficulties. Thus, the diagnosis of COVID using CT scans has been effective in assisting with RT-PCR diagnosis and severity classifications. In this paper, we proposed a new data quality control pipeline to refine the quality of CT images based on GAN and sliding windows. Also, we use class-sensitive cost functions including Label Distribution Aware Loss(LDAM Loss) and Class-balanced(CB) Loss to solve the long-tail problem existing in datasets. Our model reaches more than 0.983 MCC in the benchmark test dataset.
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