2020
DOI: 10.1109/tmi.2020.3000314
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A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images

Abstract: Segmentation of pneumonia lesions from CT scans of COVID-19 patients is important for accurate diagnosis and follow-up. Deep learning has a potential to automate this task but requires a large set of high-quality annotations that are difficult to collect. Learning from noisy training labels that are easier to obtain has a potential to alleviate this problem. To this end, we propose a novel noise-robust framework to learn from noisy labels for the segmentation task. We first introduce a noise-robust Dice loss t… Show more

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Cited by 366 publications
(304 citation statements)
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“…For example, segmentation masks can guide physician attention towards COVID-19 pneumonia lesions, and they can be used to quantify and accurately monitor the extent of lung parenchymal disease. Wang et al[ 18 ] developed a deep learning model for segmentation of COVID-19 pneumonia lesions and achieved a Dice coefficient of 0.80 on a test set of 130 CT scans. Similarly Fan et al[ 19 ] trained a deep learning model to segment consolidation and ground glass opacity separately and achieved a Dice coefficient of 0.62 and 0.46 on each task respectively.…”
Section: Initiativesmentioning
confidence: 99%
“…For example, segmentation masks can guide physician attention towards COVID-19 pneumonia lesions, and they can be used to quantify and accurately monitor the extent of lung parenchymal disease. Wang et al[ 18 ] developed a deep learning model for segmentation of COVID-19 pneumonia lesions and achieved a Dice coefficient of 0.80 on a test set of 130 CT scans. Similarly Fan et al[ 19 ] trained a deep learning model to segment consolidation and ground glass opacity separately and achieved a Dice coefficient of 0.62 and 0.46 on each task respectively.…”
Section: Initiativesmentioning
confidence: 99%
“…Wang, Deng [19] implemented a weekly supervised deep learning framework using a 3D CT image. A noise-robust framework on CT image for automatics segmentation of COVID-19 was applied by Wang, Liu [20]. Soares, Angelov [21] used an eXplainable Deep Learning approach using a CT image.…”
Section: Background Studymentioning
confidence: 99%
“…In particular, it adopts a human-in-the-loop strategy to reduce the time of manual delineation significantly. Wang et al [19] proposed noise-robust Dice loss and applied it in COPLE-Net, which surpasses other anti-noise training methods to learn COVID-19 pneumonia lesion segmentation in noisy labels. Inf-Net [20] uses a parallel partial decoder to aggregate high-level features and generate a global map to enhance the boundary area.…”
Section: B Artificial Intelligence For Covid-19 Based On Ctmentioning
confidence: 99%
“…T HE new coronavirus disease 2019 (COVID- 19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a continuing pandemic [1]- [4]. As of 9 September 2020, there have been 212 countries with outbreaks, a total of 27,486,960 cases diagnosed, and 894,983 deaths, even though the number of infected people continues to increase [5].…”
Section: Introductionmentioning
confidence: 99%