论文标题
Miniseg：高效COVID-19分段的极其最小网络
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
论文作者
论文摘要
新大流行的迅速传播，即Covid-19，严重威胁了全球健康。基于深度学习的计算机辅助筛选，例如COVID-19受感染的CT区域分割，引起了很多关注。但是，公开可用的COVID-19培训数据有限，很容易导致过度适合传统的深度学习方法，这些方法通常具有数百万个参数。另一方面，快速培训/测试和低计算成本对于快速部署和开发COVID-19筛选系统也是必需的，但是传统的深度学习方法通常在计算上是密集的。为了解决上述问题，我们提出了Miniseg，这是一种轻巧的深度学习模型，用于有效的Covid-19分割。与传统的分割方法相比，Miniseg具有多种重要的优势：i）它只有83K参数，因此不容易过度； ii）它具有较高的计算效率，因此对于实际部署很方便； iii）其他用户可以使用其私有COVID-19的数据快速重新培训，以进一步提高性能。此外，我们构建了一个全面的Covid-19分段基准，用于将Miniseg与传统方法进行比较。
The rapid spread of the new pandemic, i.e., COVID-19, has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected CT area segmentation, has attracted much attention. However, the publicly available COVID-19 training data are limited, easily causing overfitting for traditional deep learning methods that are usually data-hungry with millions of parameters. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional deep learning methods are usually computationally intensive. To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. Compared with traditional segmentation methods, MiniSeg has several significant strengths: i) it only has 83K parameters and is thus not easy to overfit; ii) it has high computational efficiency and is thus convenient for practical deployment; iii) it can be fast retrained by other users using their private COVID-19 data for further improving performance. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg to traditional methods.
