Inhalation lung injury induced by smoke bombs in children: CT manifestations, dynamic evolution features and quantitative analysis

Ma, Yanju; Zhang, Shikui; Zhao, Lianping; Zhou, Xing; Mao, Zeqing; Xu, Huaxin; Ru, Xiaorui; Huang, Gang

doi:10.21037/jtd.2018.09.84

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Some lesions have a crazy‐paving appearance and consolidation 54,55 . CT images of SARS patients demonstrate that the lesions are mainly distributed in the inferior segments of both lungs 52,56 . Most patients' lesions also appear in the peripheral lung bands 57 .…”

Section: Proposed Covid‐19 Pneumonia Data Setmentioning

confidence: 99%

“…54,55 CT images of SARS patients demonstrate that the lesions are mainly distributed in the inferior segments of both lungs. 52,56 Most patients' lesions also appear in the peripheral lung bands. 57 CT images of MERS patients show lesions mostly appear in both lungs' subpleural and basal lungs and appear as multiple GGOs and consolidation.…”

Section: Data Set Creation and Structurementioning

confidence: 99%

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Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

et al. 2021

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The goal of diagnosing the coronavirus disease 2019 (COVID‐19) from suspected pneumonia cases, that is, recognizing COVID‐19 from chest X‐ray or computed tomography (CT) images, is to improve diagnostic accuracy, leading to faster intervention. The most important and challenging problem here is to design an effective and robust diagnosis model. To this end, there are three challenges to overcome: (1) The lack of training samples limits the success of existing deep‐learning‐based methods. (2) Many public COVID‐19 data sets contain only a few images without fine‐grained labels. (3) Due to the explosive growth of suspected cases, it is urgent and important to diagnose not only COVID‐19 cases but also the cases of other types of pneumonia that are similar to the symptoms of COVID‐19. To address these issues, we propose a novel framework called Unsupervised Meta‐Learning with Self‐Knowledge Distillation to address the problem of differentiating COVID‐19 from pneumonia cases. During training, our model cannot use any true labels and aims to gain the ability of learning to learn by itself. In particular, we first present a deep diagnosis model based on a relation network to capture and memorize the relation among different images. Second, to enhance the performance of our model, we design a self‐knowledge distillation mechanism that distills knowledge within our model itself. Our network is divided into several parts, and the knowledge in the deeper parts is squeezed into the shallow ones. The final results are derived from our model by learning to compare the features of images. Experimental results demonstrate that our approach achieves significantly higher performance than other state‐of‐the‐art methods. Moreover, we construct a new COVID‐19 pneumonia data set based on text mining, consisting of 2696 COVID‐19 images (347 X‐ray + 2349 CT), 10,155 images (9661 X‐ray + 494 CT) about other types of pneumonia, and the fine‐grained labels of all. Our data set considers not only a bacterial infection or viral infection which causes pneumonia but also a viral infection derived from the influenza virus or coronavirus.

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Section: Proposed Covid‐19 Pneumonia Data Setmentioning

confidence: 99%