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Med Image Anal ; 67: 101836, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33129141


The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

/diagnóstico por imagem , Redes Neurais de Computação , Pneumonia Viral/diagnóstico por imagem , Tomografia Computadorizada por Raios X , /classificação , Humanos , Pneumonia Viral/classificação , Radiografia Torácica , Sensibilidade e Especificidade
Front Public Health ; 8: 574915, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-33330318


In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96-5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91-7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68-5.96), and age ≥60 years (HR 2.31, 95% CI 1.43-3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83-0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R 2 = 0.89) in the 7-day prediction and 0.96 (R 2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81-0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.

/epidemiologia , Progressão da Doença , Prognóstico , Medição de Risco/métodos , Medição de Risco/estatística & dados numéricos , Índice de Gravidade de Doença , Adulto , Fatores Etários , Idoso , Idoso de 80 Anos ou mais , China/epidemiologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Modelos de Riscos Proporcionais , Curva ROC , Estudos Retrospectivos , Fatores de Risco
Entropy (Basel) ; 21(8)2019 Aug 13.
Artigo em Inglês | MEDLINE | ID: mdl-33267501


This paper develops the interval maximum entropy model for the interval European option valuation by estimating an underlying asset distribution. The refined solution for the model is obtained by the Lagrange multiplier. The particle swarm optimization algorithm is applied to calculate the density function of the underlying asset, which can be utilized to price the Shanghai Stock Exchange (SSE) 50 Exchange Trades Funds (ETF) option of China and the Boeing stock option of the United States. Results show that maximum entropy distribution provides precise estimations for the underlying asset of interval number situations. In this way, we can get the distribution of the underlying assets and apply it to the interval European option pricing in the financial market.

Int J STD AIDS ; 28(4): 380-388, 2017 03.
Artigo em Inglês | MEDLINE | ID: mdl-27164966


Plasmablastic lymphoma is a rare and aggressive B cell lymphoma that is considered to be strongly associated with HIV infection. This article explores the histological morphology and immunohistochemical characteristics of HIV/AIDS-related plasmablastic lymphoma with the goal of improving the diagnosis and treatment of this rare tumor. According to criteria of the World Health Organization Classification of Tumors of Hematopoietic and Lymphoid Tissues (2008), six plasmablastic lymphoma cases admitted to the Shanghai Public Health Clinical Center were comprehensively analyzed with conventional hematoxylin-eosin staining, immunohistochemical staining and in situ hybridization. The morphological features of six tumors were consistent with PBL. Immunohistochemical staining showed that all six cases were negative for CD19, CD20, and CD79a, and positive for OCT-2, BOB-1, VS38c, and melanoma ubiquitous mutated 1. The Ki67 proliferation index was higher than 90% in all six cases. In situ hybridization indicated that four cases were EBER-positive. In addition, three cases had C-MYC translocation rearrangement. Our results showed that the immunophenotypes of PBL vary, which makes PBL diagnosis difficult. Therefore, morphological characteristics, immunophenotypic markers, and clinical data should be used in combination to enable an accurate diagnosis, especially in the presence of immunophenotypic variation, as this approach will facilitate timely treatment.

Síndrome de Imunodeficiência Adquirida/complicações , Infecções por HIV/complicações , Linfoma Relacionado a AIDS/diagnóstico , Linfoma Plasmablástico/diagnóstico , Adulto , China/epidemiologia , Feminino , Seguimentos , Humanos , Imunofenotipagem , Hibridização In Situ , Linfoma Relacionado a AIDS/complicações , Linfoma Relacionado a AIDS/patologia , Masculino , Pessoa de Meia-Idade , Linfoma Plasmablástico/complicações , Linfoma Plasmablástico/patologia
Springerplus ; 5: 647, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27330913


In this paper, we introduce hypervisor introspection, an out-of-box way to monitor the execution of hypervisors. Similar to virtual machine introspection which has been proposed to protect virtual machines in an out-of-box way over the past decade, hypervisor introspection can be used to protect hypervisors which are the basis of cloud security. Virtual machine introspection tools are usually deployed either in hypervisor or in privileged virtual machines, which might also be compromised. By utilizing hardware support including nested virtualization, EPT protection and #BP, we are able to monitor all hypercalls belongs to the virtual machines of one hypervisor, include that of privileged virtual machine and even when the hypervisor is compromised. What's more, hypercall injection method is used to simulate hypercall-based attacks and evaluate the performance of our method. Experiment results show that our method can effectively detect hypercall-based attacks with some performance cost. Lastly, we discuss our furture approaches of reducing the performance cost and preventing the compromised hypervisor from detecting the existence of our introspector, in addition with some new scenarios to apply our hypervisor introspection system.

Springerplus ; 4: 583, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-26543718


As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox's features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.