BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset

Signoroni, Alberto; Savardi, Mattia; Benini, Sergio; Adami, Nicola; Leonardi, Riccardo; Gibellini, Paolo; Vaccher, Filippo; Ravanelli, Marco; Borghesi, Andrea; Maroldi, Roberto; Farina, Davide

doi:10.1016/j.media.2021.102046

Cited by 120 publications

(107 citation statements)

References 70 publications

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“…The CT data of 674 patients from six hospitals in northern Italy were used to extract pulmonary parenchymal and vascular features. While machine learning models centered on clinical and imaging data have been proposed to aid both COVID-19 diagnosis and severity stratification [55][56][57][58][59], the inclusion of vascular features stems from an ever-larger corpus of observations that link vascular (particularly endothelial) impairment in COVID-19 [4][5][6][7][8][9] to pulmonary thromboembolism [11][12][13][16][17][18][19][20]23] and gross damage to pulmonary arterial vessels [25][26][27].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Schiaffino

Codari

Cozzi

et al. 2021

JPM

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Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

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Section: Discussionmentioning

confidence: 99%

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Schiaffino

Codari

Cozzi

et al. 2021

JPM

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“…Data after augmentation is available at ( , access date: 14 February 2021). In [ 244 ], Signoroni et al collected 4707 X-ray images for COVID-19-positive subjects collected from an Italian hospital. To maintain a robust dataset, the authors collected it from two different modalities, including (direct X-ray (DX) and computed radiology (CR)) for patients with various statuses (i.e., supine, standing, and with or without life support systems).…”

Section: Covid-19 Datasetsmentioning

confidence: 99%

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

et al. 2021

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Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

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“…Recently, one shot learning models have been proposed to detect COVID-19 using medical images. Signoroni et al [ 46 ] introduced a learning-based solution designed to assess the severity of COVID-19 disease by means of automated X-ray image processing, a domain specific implementation of [ 42 ]. Furthermore, [ 47 ] compiles an early survey of medical imaging research toward COVID-19 detection, diagnosis, and follow-up.…”

Section: Related Workmentioning

confidence: 99%

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Rochac

Zhang

Thompson

et al. 2021

Computational Intelligence and Neuroscience

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Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

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BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset

Abstract: Graphical abstract

Cited by 120 publications

References 70 publications

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

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