Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans

This paper performs a comprehensive study on the deep-learning-based computer-aided diagnosis (CADx) for the differential diagnosis of benign and malignant nodules/lesions by avoiding the potential errors caused by inaccurate image processing results (e.g., boundary segmentation), as well as the classification bias resulting from a less robust feature set, as involved in most conventional CADx algorithms. Specifically, the stacked denoising auto-encoder (SDAE) is exploited on the two CADx applications for the differentiation of breast ultrasound lesions and lung CT nodules. The SDAE architecture is well equipped with the automatic feature exploration mechanism and noise tolerance advantage, and hence may be suitable to deal with the intrinsically noisy property of medical image data from various imaging modalities. To show the outperformance of SDAE-based CADx over the conventional scheme, two latest conventional CADx algorithms are implemented for comparison. 10 times of 10-fold cross-validations are conducted to illustrate the efficacy of the SDAE-based CADx algorithm. The experimental results show the significant performance boost by the SDAE-based CADx algorithm over the two conventional methods, suggesting that deep learning techniques can potentially change the design paradigm of the CADx systems without the need of explicit design and selection of problem-oriented features

Imaging findings in coronavirus infections : SARS-CoV, MERS-CoV, and SARS-CoV-2

During the first two decades of the 21st century, there have been three coronavirus infection outbreaks raising global health concerns by severe acute respiratory syndrome coronavirus (SARS-CoV), the Middle East respiratory syndrome coronavirus (MERS-CoV), and the SARS-CoV-2. Although the reported imaging findings of coronavirus infection are variable and non-specific, the most common initial chest radiograph (CXR) and CT findings are ground-glass opacities and consolidation with peripheral predominance and eventually spread to involve both lungs as the disease progresses. These findings can be explained by the immune pathogenesis of coronavirus infection causing diffuse alveolar damage. Although it is insensitive in mild or early coronavirus infection, the CXR remains as the first-line and the most commonly used imaging modality. That is because it is rapid and easily accessible and helpful for monitoring patient progress during treatment. CT is more sensitive to detect early parenchymal lung abnormalities and disease progression, and can provide an alternative diagnosis. In this pictorial review, various coronavirus infection cases are presented to provide imaging spectrums of coronavirus infection and present differences in imaging among them or from other viral infections, and to discuss the role of imaging in viral infection outbreaks