MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset

Morozov, S. P.; Andreychenko, A. E.; Pavlov, N. A.; Vladzymyrskyy, Anton; Ледихова, Н. В.; Gombolevskiy, V. A.; Blokhin, I. A.; Gelezhe, P. B.; Gonchar, A. V.; Chernina, Valeria

doi:10.48550/arxiv.2005.06465

Cited by 31 publications

(41 citation statements)

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“…The dataset size of our study is similar to other studies [22,67]. Since public datasets typically have some limitations, such as only positive cases are available or the labels are not RT-PCR-based [51], all our CT scans were collected in our own hospital, as other studies did (e.g., [22,46,67]). Our 343 COVID-positive volumes were from patients with RT-PCR positive confirmation for the presence of SARS-CoV-2, and 202 COVID-negative volumes were collected from trauma patients undergone CT exams before the outbreak of COVID-19, and 35 COVID-negative volumes were collected in 2020 from patients without pneumonia.…”

Section: Classificationmentioning

confidence: 98%

COVID-view: Diagnosis of COVID-19 using Chest CT

Jadhav¹,

Deng²,

Zawin³

et al. 2021

Preprint

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Section: Classificationmentioning

confidence: 98%

COVID-view: Diagnosis of COVID-19 using Chest CT

Jadhav¹,

Deng²,

Zawin³

et al. 2021

Preprint

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“…In this study, the curated dataset for the CT scans came from seven recently conducted studies involving COVID-19 [1,10,30,44,46,49,66], added with another set of CXR images from Sait et al [51]. Upon investigation, the curated datasets already received preprocessing beforehand from their respective authors, alleviating the need to perform the task, as adding further changes may only lead to pre-determined outcomes, added bias, and even degrade the quality of the images.…”

Section: Dataset Acquisition and Preparationmentioning

confidence: 99%

Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans

Montalbo

2022

Multimed Tools Appl

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In such a brief period, the recent coronavirus (COVID-19) already infected large populations worldwide. Diagnosing an infected individual requires a Real-Time Polymerase Chain Reaction (RT-PCR) test, which can become expensive and limited in most developing countries, making them rely on alternatives like Chest X-Rays (CXR) or Computerized Tomography (CT) scans. However, results from these imaging approaches radiated confusion for medical experts due to their similarities with other diseases like pneumonia. Other solutions based on Deep Convolutional Neural Network (DCNN) recently improved and automated the diagnosis of COVID-19 from CXRs and CT scans. However, upon examination, most proposed studies focused primarily on accuracy rather than deployment and reproduction, which may cause them to become difficult to reproduce and implement in locations with inadequate computing resources. Therefore, instead of focusing only on accuracy, this work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects. Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to <1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other studies based on its size to performance ratio.

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“…Care Department (Morozov et al, 2020) provided 50 CT volumetric scans and corresponding annotations by experts.…”

Section: Research and Practical Clinical Center For Diagnostics And T...mentioning

confidence: 99%

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

Cui

Sun

Meng

et al. 2021

Expert Systems with Applications

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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.

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MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset

Cited by 31 publications

References 0 publications

COVID-view: Diagnosis of COVID-19 using Chest CT

COVID-view: Diagnosis of COVID-19 using Chest CT

Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans

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

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