CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning

Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19. Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and prioritizing patients by severity of the disease. In this article we adopted an approach based on using an ensemble of deep convolutional neural networks for segmentation of slices of lung CT scans. Using our models, we are able to segment the lesions, evaluate patients’ dynamics, estimate relative volume of lungs affected by lesions, and evaluate the lung damage stage. Our models were trained on data from different medical centers. We compared predictions of our models with those of six experienced radiologists, and our segmentation model outperformed most of them. On the task of classification of disease severity, our model outperformed all the radiologists.

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Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method

BMC Medical Imaging ◽

10.1186/s12880-018-0286-0 ◽

2018 ◽

Vol 18 (1) ◽

Cited By ~ 16

Author(s):

Hwejin Jung ◽

Bumsoo Kim ◽

Inyeop Lee ◽

Junhyun Lee ◽

Jaewoo Kang

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Three Dimensional ◽

Ensemble Method ◽

Ct Scans ◽

Lung Nodules ◽

Deep Convolutional Neural Networks

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Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.

10.18297/etd/3301 ◽

2019 ◽

Author(s):

Mohammad Mehdi Farhangi

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Dictionary Learning ◽

Ct Scans ◽

Lung Nodules ◽

Deep Convolutional Neural Networks ◽

Thoracic Ct

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CNN-based Classification of Degraded Images

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-028 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 28-1-28-7 ◽

Cited By ~ 1

Author(s):

Kazuki Endo ◽

Masayuki Tanaka ◽

Masatoshi Okutomi

Keyword(s):

Neural Networks ◽

Image Restoration ◽

Image Classification ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Alternative Approach ◽

Degraded Image ◽

Degraded Images ◽

Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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