GIONet: Global information optimized network for multi-center COVID-19 diagnosis via COVID-GAN and domain adversarial strategy.

The outbreak of coronavirus disease (COVID-19) in 2019 has highlighted the need for automatic diagnosis of the disease, which can develop rapidly into a severe condition. Nevertheless, distinguishing between COVID-19 pneumonia and community-acquired pneumonia (CAP) through computed tomography scans can be challenging due to their similar characteristics. The existing methods often perform poorly in the 3-class classification task of healthy, CAP, and COVID-19 pneumonia, and they have poor ability to handle the heterogeneity of multi-centers data. To address these challenges, we design a COVID-19 classification model using global information optimized network (GIONet) and cross-centers domain adversarial learning strategy. Our approach includes proposing a 3D convolutional neural network with graph enhanced aggregation unit and multi-scale self-attention fusion unit to improve the global feature extraction capability. We also verified that domain adversarial training can effectively reduce feature distance between different centers to address the heterogeneity of multi-center data, and used specialized generative adversarial networks to balance data distribution and improve diagnostic performance. Our experiments demonstrate satisfying diagnosis results, with a mixed dataset accuracy of 99.17% and cross-centers task accuracies of 86.73% and 89.61%.

Focal Lipogranuloma Inflammation: A Case Report of the Lung Origin.

INTRODUCTION: Limited experience exists regarding the clinical features of focal lipogranuloma inflammation of the lung, and there are no supporting guidelines for the diagnostic approach. CASE DESCRIPTION: We present the case of a 20-year-old man who was found to have a pulmonary nodule with a smooth margin during a health examination. He underwent wedge resection of the left lower lung to achieve a tissue diagnosis; this showed focal lipogranuloma inflammation of the lung. CONCLUSION: In this report, we combined imaging and pathological findings to reach the diagnosis, with a view to assisting clinicians in navigating this challenging clinical situation.

Cryptococcal Osteomyelitis of the Left Acetabulum: A Case Report.

BACKGROUND: Cryptococcus, as a classical "opportunistic" fungal pathogen, is capable of disseminating an invasive infection in immunocompromised hosts. The primary sites of infection include the respiratory and central nervous systems, and skeletal infection was rarely reported. In this case, we describe a case of cryptococcal osteomyelitis involving the left side of the acetabulum in a Chinese patient with chronic hepatitis B. CASE PRESENTATION: We retrospectively reviewed the case of a female (with chronic hepatitis B) with left acetabulum pain and limited mobility, with fever occurring during the infection who presented to the Fifth Affiliated Hospital of Zunyi Medical University. Upon imaging, we found osteolytic bone destruction in the left acetabulum with inflammatory changes in the surrounding bone and soft tissue, accompanied by abscess formation. Following an 11-month of antifungal therapy, the clinical symptoms improved and the lesion area reduced in size. In addition, there was no sign of recurrence. CONCLUSION: Cryptococcus infections should be considered in the differential diagnosis of infectious osteolytic bone lesions, particularly when patients with immune insufficiency. Pathological examinations and fungal cultures are essential to provide a differential diagnosis.