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

by   Qiangguo Jin, et al.

The capability of generalization to unseen domains is crucial for deep learning models when considering real-world scenarios. However, current available medical image datasets, such as those for COVID-19 CT images, have large variations of infections and domain shift problems. To address this issue, we propose a prior knowledge driven domain adaptation and a dual-domain enhanced self-correction learning scheme. Based on the novel learning schemes, a domain adaptation based self-correction model (DASC-Net) is proposed for COVID-19 infection segmentation on CT images. DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine segmentation results. The innovations in AFD-DA include an image-level activation feature extractor with attention to lung abnormalities and a multi-level discrimination module for hierarchical feature domain alignment. The proposed self-correction learning process adaptively aggregates the learned model and corresponding pseudo labels for the propagation of aligned source and target domain information to alleviate the overfitting to noises caused by pseudo labels. Extensive experiments over three publicly available COVID-19 CT datasets demonstrate that DASC-Net consistently outperforms state-of-the-art segmentation, domain shift, and coronavirus infection segmentation methods. Ablation analysis further shows the effectiveness of the major components in our model. The DASC-Net enriches the theory of domain adaptation and self-correction learning in medical imaging and can be generalized to multi-site COVID-19 infection segmentation on CT images for clinical deployment.



There are no comments yet.


page 1

page 2

page 3

page 5

page 6

page 10

page 15

page 19


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

The outbreak of novel coronavirus disease 2019 (COVID-19) has already in...

PnP-AdaNet: Plug-and-Play Adversarial Domain Adaptation Network with a Benchmark at Cross-modality Cardiac Segmentation

Deep convolutional networks have demonstrated the state-of-the-art perfo...

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

Self-training is a competitive approach in domain adaptive segmentation,...

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

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

Image-level Harmonization of Multi-Site Data using Image-and-Spatial Transformer Networks

We investigate the use of image-and-spatial transformer networks (ISTNs)...

Supervised Segmentation with Domain Adaptation for Small Sampled Orbital CT Images

Deep neural networks (DNNs) have been widely used for medical image anal...

Online unsupervised Learning for domain shift in COVID-19 CT scan datasets

Neural networks often require large amounts of expert annotated data to ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.