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

Cui, Hui; Sun, Changming; Meng, Zhaopeng; Wei, Leyi; Su, Ran

doi:10.1016/j.eswa.2021.114848

Cited by 38 publications

(32 citation statements)

References 65 publications

(97 reference statements)

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“…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.…”

Section: Comparisons With the State-of-the-art Methodsmentioning

confidence: 58%

“…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.…”

Section: Comparisons With the State-of-the-art Methodsmentioning

confidence: 99%

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

Owais

Baek

Park

2021

JPM

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Background: Early and accurate detection of COVID-19-related findings (such as well-aerated regions, ground-glass opacity, crazy paving and linear opacities, and consolidation in lung computed tomography (CT) scan) is crucial for preventive measures and treatment. However, the visual assessment of lung CT scans is a time-consuming process particularly in case of trivial lesions and requires medical specialists. Method: A recent breakthrough in deep learning methods has boosted the diagnostic capability of computer-aided diagnosis (CAD) systems and further aided health professionals in making effective diagnostic decisions. In this study, we propose a domain-adaptive CAD framework, namely the dilated aggregation-based lightweight network (DAL-Net), for effective recognition of trivial COVID-19 lesions in CT scans. Our network design achieves a fast execution speed (inference time is 43 ms on a single image) with optimal memory consumption (almost 9 MB). To evaluate the performances of the proposed and state-of-the-art models, we considered two publicly accessible datasets, namely COVID-19-CT-Seg (comprising a total of 3520 images of 20 different patients) and MosMed (including a total of 2049 images of 50 different patients). Results: Our method exhibits average area under the curve (AUC) up to 98.84%, 98.47%, and 95.51% for COVID-19-CT-Seg, MosMed, and cross-dataset, respectively, and outperforms various state-of-the-art methods. Conclusions: These results demonstrate that deep learning-based models are an effective tool for building a robust CAD solution based on CT data in response to present disaster of COVID-19.

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Section: Comparisons With the State-of-the-art Methodsmentioning

confidence: 58%

Section: Comparisons With the State-of-the-art Methodsmentioning

confidence: 99%

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

Owais

Baek

Park

2021

JPM

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Taresh

Zhu

Ali

et al. 2021

PeerJ Computer Science

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The emergence of the novel coronavirus pneumonia (COVID-19) pandemic at the end of 2019 led to worldwide chaos. However, the world breathed a sigh of relief when a few countries announced the development of a vaccine and gradually began to distribute it. Nevertheless, the emergence of another wave of this pandemic returned us to the starting point. At present, early detection of infected people is the paramount concern of both specialists and health researchers. This paper proposes a method to detect infected patients through chest x-ray images by using the large dataset available online for COVID-19 (COVIDx), which consists of 2128 X-ray images of COVID-19 cases, 8,066 normal cases, and 5,575 cases of pneumonia. A hybrid algorithm is applied to improve image quality before undertaking neural network training. This algorithm combines two different noise-reduction filters in the image, followed by a contrast enhancement algorithm. To detect COVID-19, we propose a novel convolution neural network (CNN) architecture called KL-MOB (COVID-19 detection network based on the MobileNet structure). The performance of KL-MOB is boosted by adding the Kullback–Leibler (KL) divergence loss function when trained from scratch. The KL divergence loss function is adopted for content-based image retrieval and fine-grained classification to improve the quality of image representation. The results are impressive: the overall benchmark accuracy, sensitivity, specificity, and precision are 98.7%, 98.32%, 98.82% and 98.37%, respectively. These promising results should help other researchers develop innovative methods to aid specialists. The tremendous potential of the method proposed herein can also be used to detect COVID-19 quickly and safely in patients throughout the world.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

Chen

et al. 2021

Expert Systems with Applications

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Millions of positive COVID-19 patients are suffering from the pandemic around the world, a critical step in the management and treatment is severity assessment, which is quite challenging with the limited medical resources. Currently, several artificial intelligence systems have been developed for the severity assessment. However, imprecise severity assessment and insufficient data are still obstacles. To address these issues, we proposed a novel deep-learning-based framework for the fine-grained severity assessment using 3D CT scans, by jointly performing lung segmentation and lesion segmentation. The main innovations in the proposed framework include: 1) decomposing 3D CT scan into multi-view slices for reducing the complexity of 3D model, 2) integrating prior knowledge (dual-Siamese channels and clinical metadata) into our model for improving the model performance. We evaluated the proposed method on 1301 CT scans of 449 COVID-19 cases collected by us, our method achieved an accuracy of 86.7% for four-way classification, with the sensitivities of 92%, 78%, 95%, 89% for four stages. Moreover, ablation study demonstrated the effectiveness of the major components in our model. This indicates that our method may contribute a potential solution to severity assessment of COVID-19 patients using CT images and clinical metadata.

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

Cited by 38 publications

References 65 publications

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

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

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

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

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