Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning

Abstract

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.

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• Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
• Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging
• Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation
• COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
• A Review of Automated Diagnosis of COVID-19 Based on Scanning Images
• Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan
• FLANNEL: Focal Loss Based Neural Network Ensemble for COVID-19 Detection
• Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review
• Triple-view Convolutional Neural Networks for COVID-19 Diagnosis with Chest X-ray
• CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans
• Few-shot Learning for CT Scan based COVID-19 Diagnosis
• RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection