COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19

by   Yifan Zhang, et al.

The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Most COVID-19 patients suffer from lung infection, so one important diagnostic method is to screen chest radiography images, e.g., X-Ray or CT images. However, such examinations are time-consuming and labor-intensive, leading to limited diagnostic efficiency. To solve this issue, AI-based technologies, such as deep learning, have been used recently as effective computer-aided means to improve diagnostic efficiency. However, one practical and critical difficulty is the limited availability of annotated COVID-19 data, due to the prohibitive annotation costs and urgent work of doctors to fight against the pandemic. This makes the learning of deep diagnosis models very challenging. To address this, motivated by that typical pneumonia has similar characteristics with COVID-19 and many pneumonia datasets are publicly available, we propose to conduct domain knowledge adaptation from typical pneumonia to COVID-19. There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19. To address them, we propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA. Specifically, we alleviate the domain discrepancy via feature adversarial adaptation and handle the task difference issue via a novel classifier separation scheme. In this way, COVID-DA is able to diagnose COVID-19 effectively with only a small number of COVID-19 annotations. Extensive experiments verify the effectiveness of COVID-DA and its great potential for real-world applications.


page 1

page 3

page 6


Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images

The capability of generalization to unseen domains is crucial for deep l...

DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs

Automated infection measurement and COVID-19 diagnosis based on Chest X-...

Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis

We introduce a new dataset called Synthetic COVID-19 Chest X-ray Dataset...

SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation

The global pandemic of COVID-19 has infected millions of people since it...

Few-shot Learning for CT Scan based COVID-19 Diagnosis

Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of Inte...

MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation

The rapid spread of the new pandemic, coronavirus disease 2019 (COVID-19...

From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example

Supervised learning tends to produce more accurate classifiers than unsu...

Please sign up or login with your details

Forgot password? Click here to reset