Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Nov 2018
Trends and characteristics of global medical informatics conferences from 2007 to 2017: A bibliometric comparison of conference publications from Chinese, American, European and the Global Conferences.
As the second-largest economy in the world, China has invested considerable financial and policy support into hospital informatization since health care reform in 2010. However, the results and experience of such investments have not been compared with relevant research and applications in the United States and Europe. ⋯ The MI annual conferences are all different among China, the United States and Europe. China lags in the theoretical and discipline bases but has made considerable investments in the past 10 years. China should fully use its second-mover advantage and application advantages and utilize international experiences and cooperation to make stronger contributions to global MI development.
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Comput Methods Programs Biomed · Nov 2018
Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.
Nowadays, getting an efficient Brain Tumor Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical diagnosis, treatment and follow-up. The aim of this study is to develop a new deep learning model for the segmentation of brain tumors. The proposed models are used to segment the brain tumors of Glioblastomas (with both high and low grade). Glioblastomas have four properties: different sizes, shapes, contrasts, in addition, Glioblastomas appear anywhere in the brain. ⋯ The proposed deep learning models are effective for the segmentation of brain tumors and allow to obtain high accurate results. Moreover, the proposed models could help the physician experts to reduce the time of diagnostic.
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Comput Methods Programs Biomed · Oct 2018
Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.
In pulmonary nodule detection, the first stage, candidate detection, aims to detect suspicious pulmonary nodules. However, detected candidates include many false positives and thus in the following stage, false positive reduction, such false positives are reliably reduced. Note that this task is challenging due to 1) the imbalance between the numbers of nodules and non-nodules and 2) the intra-class diversity of non-nodules. Although techniques using 3D convolutional neural networks (CNNs) have shown promising performance, they suffer from high computational complexity which hinders constructing deep networks. To efficiently address these problems, we propose a novel framework using the ensemble of 2D CNNs using single views, which outperforms existing 3D CNN-based methods. ⋯ We presented a novel false positive reduction framework, the ensemble of single-view 2D CNNs with fully automatic non-nodule categorization, for pulmonary nodule detection. Unlike previous 3D CNN-based frameworks, we utilized 2D CNNs using 2D single views to improve computational efficiency. Also, our training scheme using categorized non-nodules, extends the learning capability of representative features of different non-nodules. Our framework achieved state-of-the-art performance with low computational complexity.
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Comput Methods Programs Biomed · Aug 2018
Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
Among all malignant tumors, lung cancer ranks in the top in mortality rate. Pulmonary nodule is the early manifestation of lung cancer, and plays an important role in its discovery, diagnosis and treatment. The technology of medical imaging has encountered a rapid development in recent years, thus the amount of pulmonary nodules can be discovered are on the raise, which means even tiny or minor changes in lung can be recorded by the CT images. This paper proposes a pulmonary nodule computer aided diagnosis (CAD) based on semi-supervised extreme learning machine(SS-ELM). ⋯ We have proposed a pulmonary nodule CAD system based on SS-ELM, which achieving better generalization performance at faster learning speed and higher testing accuracy than ELM, SVM, PNN and MLP. The SS-ELM based pulmonary nodules CAD has been proposed to solve the problem of uncertain class data using.
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Comput Methods Programs Biomed · Jul 2018
A computerized method for evaluating scoliotic deformities using elliptical pattern recognition in X-ray spine images.
Several studies have evaluated the reproducibility of the Cobb angle for measuring the degree of scoliotic deformities from X-ray spine images, and proposed different geometric models for describing the spinal curvature. The ellipse was shown to be an adequate geometric form, but was not yet applied for the identification and quantification of scoliotic curvatures. The purpose of this study is therefore to propose and validate a novel computerized methodology for the detection of elliptical patterns from X-ray images to evaluate the extent of the underlying scoliotic deformity. ⋯ The results indicate that the proposed computerized methodology is able to reliably reproduce scoliotic curvatures using the geometric parameters of the underlying ellipses. In comparison to conventional approaches, the proposed methodology potentially produces less errors, requires a relatively low observer interaction, takes into account all vertebrae within the observed scoliotic deformity, and allows for both qualitative and quantitative evaluations that may complement the diagnosis, study and treatment of scoliosis.