Zhou Jieli, Jing Baoyu, Wang Zeya, Xin Hongyi, Tong Hanghang
IEEE/ACM Trans Comput Biol Bioinform. 2022 Sep-Oct;19(5):2605-2612. doi: 10.1109/TCBB.2021.3066331.
Due to the shortage of COVID-19 viral testing kits, radiology imaging is used to complement the screening process. Deep learning based methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance for the CNN model due to two issues, first the large domain shift present in chest x-ray datasets and second the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these two important issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present initial results showing that SODA can produce better pathology localizations in the chest x-rays.
由于新冠病毒检测试剂盒短缺，放射影像学被用于辅助筛查过程。基于深度学习的方法在胸部X光图像中自动检测新冠肺炎方面很有前景。这些工作大多首先在现有的大规模胸部X光图像数据集上训练卷积神经网络（CNN），然后在新收集的新冠肺炎胸部X光数据集上对模型进行微调，而该数据集的规模通常要小得多。然而，由于两个问题，简单的微调可能会导致CNN模型性能不佳，一是胸部X光数据集存在较大的领域差异，二是新冠肺炎胸部X光数据集规模相对较小。为了解决这两个重要问题，我们在半监督开放集领域适应设置中构建了新冠肺炎胸部X光图像分类问题，并提出了一种新颖的领域适应方法，即半监督开放集领域对抗网络（SODA）。SODA旨在使不同领域的数据分布在通用领域空间以及源数据和目标数据的公共子空间中对齐。在我们的实验中，与最近的最先进模型相比，SODA在区分新冠肺炎和普通肺炎方面取得了领先的分类性能。我们还展示了初步结果，表明SODA能够在胸部X光片中产生更好的病理定位。
