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

arXiv: https://arxiv.org/abs/2005.03227 · HTML: https://mlsanity.com/paper/2005.03227/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 15 of 15)

- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2203.05880/) (2022)
- [Detection and severity classification of COVID-19 in CT images using deep learning](https://mlsanity.com/paper/2102.07726/) (2021)
- [Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation](https://mlsanity.com/paper/2109.03478/) (2021)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [RCoNet: Deformable Mutual Information Maximization and High-order Uncertainty-aware Learning for Robust COVID-19 Detection](https://mlsanity.com/paper/2102.11099/) (2021)
- [Few-shot Learning for CT Scan based COVID-19 Diagnosis](https://mlsanity.com/paper/2102.00596/) (2021)
- [Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images](https://mlsanity.com/paper/2104.09699/) (2021)
- [Dual Multi-scale Mean Teacher Network for Semi-supervised Infection Segmentation in Chest CT Volume for COVID-19](https://mlsanity.com/paper/2211.05548/) (2022)
- [Navigating Conflicting Views: Harnessing Trust for Learning](https://mlsanity.com/paper/2406.00958/) (2024)
- [MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNN](https://mlsanity.com/paper/2102.07911/) (2021)
- [Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window](https://mlsanity.com/paper/2206.03803/) (2022)
- [BrainNetDiff: Generative AI Empowers Brain Network Generation via Multimodal Diffusion Model](https://mlsanity.com/paper/2311.05199/) (2023)
- [A Tumor Aware DenseNet Swin Hybrid Learning with Boosted and Hierarchical Feature Spaces for Large-Scale Brain MRI Classification](https://mlsanity.com/paper/2601.18330/) (2026)
- [Multi-modal Graph Learning for Disease Prediction](https://mlsanity.com/paper/2107.00206/) (2021)
- [Cross-dataset domain adaptation for the classification COVID-19 using chest computed tomography images](https://mlsanity.com/paper/2311.08524/) (2023)

## Abstract (arXiv)

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.