COVID-19 detection based on self-supervised transfer learning using chest X-ray images.

PURPOSE: Considering several patients screened due to COVID-19 pandemic, computer-aided detection has strong potential in assisting clinical workflow efficiency and reducing the incidence of infections among radiologists and healthcare providers. Since many confirmed COVID-19 cases present radiological findings of pneumonia, radiologic examinations can be useful for fast detection. Therefore, chest radiography can be used to fast screen COVID-19 during the patient triage, thereby determining the priority of patient's care to help saturated medical facilities in a pandemic situation. METHODS: In this paper, we propose a new learning scheme called self-supervised transfer learning for detecting COVID-19 from chest X-ray (CXR) images. We compared six self-supervised learning (SSL) methods (Cross, BYOL, SimSiam, SimCLR, PIRL-jigsaw, and PIRL-rotation) with the proposed method. Additionally, we compared six pretrained DCNNs (ResNet18, ResNet50, ResNet101, CheXNet, DenseNet201, and InceptionV3) with the proposed method. We provide quantitative evaluation on the largest open COVID-19 CXR dataset and qualitative results for visual inspection. RESULTS: Our method achieved a harmonic mean (HM) score of 0.985, AUC of 0.999, and four-class accuracy of 0.953. We also used the visualization technique Grad-CAM++ to generate visual explanations of different classes of CXR images with the proposed method to increase the interpretability. CONCLUSIONS: Our method shows that the knowledge learned from natural images using transfer learning is beneficial for SSL of the CXR images and boosts the performance of representation learning for COVID-19 detection. Our method promises to reduce the incidence of infections among radiologists and healthcare providers.

Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.

PROBLEM: Detecting COVID-19 from chest X-ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. AIM: In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. METHODS: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn distinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy. RESULTS: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. CONCLUSION: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists.

Case Report: Rehabilitation for Lower Extremity Pain Due to Venous Stasis in a Patient With Multisystem Inflammatory Syndrome in Children.

Recently, it was reported that children recovering from coronavirus disease (COVID-19) developed multisystem inflammatory syndrome in children (MIS-C), which causes severe inflammation in multiple organs of the body. Because MIS-C is a new disease, the pathophysiology and prognosis are unknown. Owing to a lack of studies on this subject, we herein provide information on rehabilitation for children with MIS-C. A 12-year-old male patient presented with systemic inflammatory symptoms after approximately 2 months since recovery from COVID-19. He was treated with cyclosporine and steroid pulse therapy after admission to our hospital. His general condition improved significantly within approximately 1 week. Thereafter, his lower legs turned dark purple and he experienced intense pain whenever the lower limbs hung below the heart, such as in the sitting position. The patient was referred to the rehabilitation department, as he had difficulties during standing and walking. Because the symptoms improved with elevation of the lower extremities, we considered that the pain was related to venous stasis. The pain reduced when an elastic bandage was applied for the prevention of venous stasis; therefore, exercise therapy was implemented while the patient wore the elastic bandage. The patient's lower extremity symptoms improved in 10 days. He was discharged after 16 days and could independently perform activities of daily living (ADL). The mechanism underlying the patient's pain could not be determined; however, rehabilitation was effective when combined with compression therapy using an elastic bandage.