论文标题
黑色的光：COVID-19 CT的语义分割的数据增强技术的评估
Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation
论文作者
论文摘要
借助19日的全球大流行，医学图像的计算机辅助诊断引起了很多关注，并且非常需要计算机层析成像（CT）的语义分割方法。 CT的语义分割是COVID-19自动检测的许多研究领域之一，自Covid-19爆发以来，已广泛探索。在这项工作中，我们提出了对不同数据增强技术如何改善有关此问题的编码器神经网络的培训的广泛分析。在五个不同的数据集上评估了二十种不同的数据增强技术。每个数据集通过五倍的交叉验证策略进行了验证，从而导致超过3,000个实验。我们的发现表明，空间级别的转换是改善有关此问题神经网络学习的最有希望的。
With the COVID-19 global pandemic, computer-assisted diagnoses of medical images have gained much attention, and robust methods of Semantic Segmentation of Computed Tomography (CT) became highly desirable. Semantic Segmentation of CT is one of many research fields of automatic detection of COVID-19 and has been widely explored since the COVID-19 outbreak. In this work, we propose an extensive analysis of how different data augmentation techniques improve the training of encoder-decoder neural networks on this problem. Twenty different data augmentation techniques were evaluated on five different datasets. Each dataset was validated through a five-fold cross-validation strategy, thus resulting in over 3,000 experiments. Our findings show that spatial level transformations are the most promising to improve the learning of neural networks on this problem.
