CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning
Not on the map. Why?
Abstract (arXiv)
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 prioritizingpatients by severity of the disease. In this paper we adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of slices of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage. Our modelswere trained on data from different medical centers. We compared predictions of our models with those of six experiencedradiologists and our segmentation model outperformed most of them. On the task of classification of disease severity, ourmodel outperformed all the radiologists.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 20 of its 47 references that are in the map. First posted on arXiv 25 May 2021.
Builds on
20 of 47 references.
References · 20
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2016Feature Pyramid Networks for Object Detectioncited by 5,036
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,186
- 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 461
- 2017Measuring Catastrophic Forgetting in Neural Networkscited by 360
- 2020Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Imagescited by 184
- 2020Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scanscited by 158
- 2017Dual Path Networkscited by 119
- 2020MosMedData: Chest CT Scans With COVID-19 Related Findings Datasetcited by 104
8 more
- 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 95
- 2018Unsupervised domain adaptation for medical imaging segmentation with self-ensemblingcited by 94
- 2020JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentationcited by 52
- 2020Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Imagescited by 43
- 2020Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumoniacited by 26
- 2020Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rayscited by 21
- 2020MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentationcited by 18
- 2020Radiologist-level stroke classification on non-contrast CT scans with Deep U-Netcited by 3
Built on by
Citing papers per year: 2021: 1.
1 paper.