Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation

Xu, Geng-Xin; Liu, Chen; Liu, Jun; Ding, Zhongxiang; Shi, Feng; Guo, Man; Zhao, Wei; Li, Xiaoming; Wei, Ying; Gao, Yaozong; Ren, Chuan-Xian; Shen, Dinggang

doi:10.1109/tmi.2021.3104474

Cited by 33 publications

(16 citation statements)

References 55 publications

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“…The COVID-19 diagnosis is confirmed by the positive results of the nucleic acid amplification test of the respiratory tract or blood specimens using reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) [7,8]. However, methods like chest X-ray (CXR) and chest Computed Tomography (CT) scan are medical imaging techniques, which are widely used to assess the pneumonia due to COVID-19 [9][10][11][12][13][14][15][16][17][18][19]. The reported sensitivity of CXR for COVID-19 pneumonia is relatively low in the early phase of the disease and in mild cases (69%).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco

Grassi

Granata

et al. 2021

JPM

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Objective: To report an overview and update on Artificial Intelligence (AI) and COVID-19 using chest Computed Tomography (CT) scan and chest X-ray images (CXR). Machine Learning and Deep Learning Approaches for Diagnosis and Treatment were identified. Methods: Several electronic datasets were analyzed. The search covered the years from January 2019 to June 2021. The inclusion criteria were studied evaluating the use of AI methods in COVID-19 disease reporting performance results in terms of accuracy or precision or area under Receiver Operating Characteristic (ROC) curve (AUC). Results: Twenty-two studies met the inclusion criteria: 13 papers were based on AI in CXR and 10 based on AI in CT. The summarized mean value of the accuracy and precision of CXR in COVID-19 disease were 93.7% ± 10.0% of standard deviation (range 68.4–99.9%) and 95.7% ± 7.1% of standard deviation (range 83.0–100.0%), respectively. The summarized mean value of the accuracy and specificity of CT in COVID-19 disease were 89.1% ± 7.3% of standard deviation (range 78.0–99.9%) and 94.5 ± 6.4% of standard deviation (range 86.0–100.0%), respectively. No statistically significant difference in summarized accuracy mean value between CXR and CT was observed using the Chi square test (p value > 0.05). Conclusions: Summarized accuracy of the selected papers is high but there was an important variability; however, less in CT studies compared to CXR studies. Nonetheless, AI approaches could be used in the identification of disease clusters, monitoring of cases, prediction of the future outbreaks, mortality risk, COVID-19 diagnosis, and disease management.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Fusco

Grassi

Granata

et al. 2021

JPM

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“…In practice, visual discrepancies among the clinical datasets could be large [39,40,41,42]. For example, DRIVE dataset contains 33 images without any sign of diabetic retinopathy and 7 images with signs of mild early diabetic retinopathy.…”

Section: Evaluation On Cross-datasetsmentioning

confidence: 99%

SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss

Xu¹,

Ren²

2022

Preprint

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Segmentation of retinal vessel images is critical to the diagnosis of retinopathy. Recently, convolutional neural networks have shown significant ability to extract the blood vessel structure. However, it remains challenging to refined segmentation for the capillaries and the edges of retinal vessels due to thickness inconsistencies and blurry boundaries. In this paper, we propose a novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss (SPNet) to address the above problems. Specifically, we introduce a decoder-sharing mechanism to capture multi-scale semantic information, where feature maps at diverse scales are decoded through a sequence of weight-sharing decoder modules. Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase. A pyramid-like loss function is designed to compensate possible segmentation errors progressively. Experimental results on public benchmarks show that the proposed method outperforms the backbone network and the state-of-the-art methods, especially in the regions of the capillaries and the vessel contours. In addition, performances on cross-datasets verify that SPNet shows stronger generalization ability.

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“…Exploring the invariant representations across domains is vital for UDA. Recently, UDA is receiving more attention due to its widespread applications in object recognition [5], object detection [6], speech recognition [7], disease diagnosis [8] and so on, which promote the growth of industry greatly.…”

Section: Introductionmentioning

confidence: 99%

Maximizing Conditional Independence for Unsupervised Domain Adaptation

Zhai¹,

Luo²

2022

Preprint

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Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions. Existing methods mainly focus on matching the marginal distributions of the source and target domains, which probably lead a misalignment of samples from the same class but different domains. In this paper, we deal with this misalignment by achieving the class-conditioned transferring from a new perspective. We aim to maximize the conditional independence of feature and domain given class in the reproducing kernel Hilbert space. The optimization of the conditional independence measure can be viewed as minimizing a surrogate of a certain mutual information between feature and domain. An interpretable empirical estimation of the conditional dependence is deduced and connected with the unconditional case. Besides, we provide an upper bound on the target error by taking the class-conditional distribution into account, which provides a new theoretical insight for most class-conditioned transferring methods. In addition to unsupervised domain adaptation, we extend our method to the multi-source scenario in a natural and elegant way. Extensive experiments on four benchmarks validate the effectiveness of the proposed models in both unsupervised domain adaptation and multiple source domain adaptation.

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Cross-Site Severity Assessment of COVID-19 From CT Images via Domain Adaptation

Cited by 33 publications

References 55 publications

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment

SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss

Maximizing Conditional Independence for Unsupervised Domain Adaptation

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