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

Abstract

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.

Domain-Adaptive Artificial Intelligence-Based Model for Personalized Diagnosis of Trivial Lesions Related to COVID-19 in Chest Computed Tomography Scans

“…In conclusion, these comparative results (Table 4) highlight the superior performance of our model over all baseline models [22,29,[44][45][46]. Moreover, there are some existing studies [12][13][14]41,[47][48][49][50] that provide state-of-theart benchmarks for our selected datasets. Therefore, we also compared the results of our methods with those of these methods [12][13][14]41,[47][48][49][50], which are given in Table 5.…”

“…Moreover, there are some existing studies [12][13][14]41,[47][48][49][50] that provide state-of-theart benchmarks for our selected datasets. Therefore, we also compared the results of our methods with those of these methods [12][13][14]41,[47][48][49][50], which are given in Table 5. First, Zhang et al [12] proposed a new variant of C-GAN, called CoSinGAN, with the capability to be learned from a single image and synthesize high-quality CT images for efficient training of a segmentation model.…”

KL-MOB: automated COVID-19 recognition using a novel approach based on image enhancement and a modified MobileNet CNN

“…Since only a limited number of CXR images are available for COVID-19 infection, out-of-distribution issues may arise, so more data from related distributions is needed for further evaluation. There are several techniques that would be another way to overcome this problem, include, but are not limited to data augmentation techniques ( Chaudhari, Agrawal & Kotecha, 2019 ), transfer learning ( Taresh et al, 2021 ; Bhatt, Ganatra & Kotecha, 2021a ), domain-adaptation ( Zhang et al, 2020 ; Jin et al, 2021 ) and adversarial learning ( Goel et al, 2021 ; Rahman et al, 2021a ; Motamed, Rogalla & Khalvati, 2021 ), etc. Finally, the image enhancement must be verified by a radiologist, which we have not yet been able to do due to the emerging conditions.…”

A deep-learning-based framework for severity assessment of COVID-19 with CT images

“…Thirdly, few models incorporated prior knowledge with feature extraction and discrimination process. Prior knowledge, however, can regularize the model and improve the model performance especially with the limited number of training samples ( Jin, et al, 2021 ). To address these obstacles, we proposed a novel prior-knowledge-based artificial intelligence (AI) system for fine-grained severity assessment of COVID-19.…”