SODA: Detecting COVID-19 in Chest X-Rays With Semi-Supervised Open Set Domain Adaptation

Abstract

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, firstly the large domain shift present in chest x-ray datasets and secondly 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.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

“…Furthermore, other authors advised the sampling of large datasets to reduce predictive uncertainty, even though most works used small image samples, due to the lack of large open COVID-19 datasets (particularly for CXR) [139][140][141][142]. This is why further studies are needed to implement AI capacities in the above discussed settings (identification, screening, patients' stratification and differential diagnosis), in order to guide the development of AI-empowered tools to reduce human error and assist radiologists in their decision-making process.…”

Semi-supervised Learning for COVID-19 Image Classification via ResNet

“…The rationale of these semi-supervised learning models is to enrich the supervision signals by exploiting the knowledge learned on unlabeled data [41], or regularize the network by enforcing smooth and consistent classification boundaries [40]. Regarding COVID-19 research such as COVID-19 image classification and image segmentation, semi-supervised learning is employed to resolve the lacking of labeled data [42][43][44][45][46][47]. However, for COVID-19 image classification, these studies [42][43][44] have not comprehensively examined the model performance on a large-scale of X-ray image dataset such as COVIDx [20] by comparing with the state-of-the-art, especially for the case of very few labeled data such as less than 10% labeled data.…”

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

“…Particularly, a seven-category lung-lesion segmentation model is deployed for ROI mask and the obtained lung-lesion map is fed to a deep model for COVID-19 diagnosis. Similarly, Wang B. et al (2021) have introduced a diagnosis system consisting of a segmentation model and a classification model. The segmentation model detects ROI from lung scans and then the classification model determines if it is associated with COVID-19 for each lesion region.…”

“…It takes the numerical representations from pre-trained ChXNet as input and innovates a non-iterative mapping for sparse representation learning. In addition, Zhou et al (2021) have considered the problem of COVID-19 CXR image classification in a semi-supervised domain adaptation setting and proposed a novel domain adaptation method, namely, semi-supervised open set domain adversarial network (SODA). It aligns data distributions in different domains through domain adversarial training ( Ganin et al, 2016 ).…”