다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

(1977). Markov Chains — Theory and Applications. Journal of the Operational Research Society: Vol. 28, Volume 28, issue 1, pp. 236-237.

Markov Chains — Theory and Applications

Objective@#To explore imaging characteristics of children with 2019 novel coronavirus (2019-nCoV) infection.@*Methods@#A retrospective analysis was performed on clinical data and chest CT images of 15 children diagnosed with 2019-nCoV. They were admitted to the third people’s Hospital of Shenzhen from January 16 to February 6, 2020. The distribution and morphology of pulmonary lesions on chest CT images were analyzed.@*Results@#Among the 15 children, there were 5 males and 10 females, aged from 4 to 14 years old. Five of the 15 children were febrile and 10 were asymptomatic on first visit. The first nasal or pharyngeal swab samples in all the 15 cases were positive for 2019-nCoV nucleic acid. For their first chest CT images, 6 patients had no lesions, while 9 patients had pulmonary inflammation lesions. Seven cases of small nodular ground glass opacities and 2 cases of speckled ground glass opacities were found. After 3 to 5 days of treatment, 2019-nCoV nucleic acid in a second respiratory sample turned negative in 6 cases. Among them, chest CT images showed less lesions in 2 cases, no lesion in 3 cases, and no improvement in 1 case. Other 9 cases were still positive in a second nucleic acid test. Six patients showed similar chest CT inflammation, while 3 patients had new lesions, which were all small nodular ground glass opacities.@*Conclusions@#The early chest CT images of children with 2019-nCoV infection are mostly small nodular ground glass opacities. The clinical symptoms of children with 2019-nCoV infection are nonspecific. Dynamic reexamination of chest CT and nucleic acid are important.

Analysis of CT features of 15 Children with 2019 novel coronavirus infection/ 中华儿科杂志/ Analysis of CT features of 15 Children with 2019 novel coronavirus infection/ 中华儿科杂志

Spatial-based skin detection using discriminative skin-presence features

Introduction.- Data-Driven Discovery of Physical, Chemical, and Pharmaceutical Materials.- Cross-Validation and Inference in Bioinformatics/Cancer Genomics.- Applying MQSPRs - New Challenges and Opportunities.- Data Mining in Materials Science.- Data Science in the Defense Establishment.- Combining Heuristic and Physics-Based Methods for Predicting Nanocomposite Properties.- From Ferroelectrics to Fuel Cells: In Search of Descriptors for the Transport Properties of Complex Oxides.- Computationally Driven Targeting of Advanced Thermoelectric Materials.- The MGI, Materials Informatics, and NIST) Microstructure Informatics for Mining Structure-Property-Processing Linkages.- A Genomic Approach to Properties of MAX Phase Compounds.- Accelerating Discovery of Complex Formulations and Molecules.- Optimal Learning for Discovering Minimal Peptide Substrates.- Model-based Classification: Predictive and Optimal.

Information science for materials discovery and design

In recent years, processing the images that contain human faces has been a growing research interest because of establishment and development of automatic methods especially in security applications, compression, and perceptual user interface. In this paper, a new method has been proposed for multiple face detection and tracking in video frames. The proposed method uses skin color, edge and shape information, face detection, and dynamic movement analysis of faces for more accurate real-time multiple face detection and tracking purposes. One of the main advantages of the proposed method is its robustness against usual challenges in face tracking such as scaling, rotation, scene changes, fast movements, and partial occlusions.

Real-time multiple face detection and tracking

Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images.
 PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020. Studies that evaluated the performance of DL-based models in the segmentation, and/or classification and/or grading of HNCs and/or brain tumors were included. Selected studies for each analysis were statistically evaluated based on the diagnostic performance metrics. The search results retrieved 1,664 related studies, of which 30 studies were eligible for meta-analysis. The overall performance of DL models for the complete tumor in terms of the pooled Dice score, sensitivity, and specificity was 0.8965 (95% confidence interval (95% CI): 0.76–0.9994), 0.9132 (95% CI: 0.71–0.994) and 0.9164 (95% CI: 0.78–1.00), respectively. The DL methods achieved the highest performance for classifying three types of glioma, meningioma, and pituitary tumors with overall accuracies of 96.01%, 99.73%, and 96.58%, respectively. Stratification of glioma tumors by high and low grading revealed overall accuracies of 94.32% and 94.23% for the DL methods, respectively. Based on the obtained results, we can acknowledge the significant ability of DL methods in the mentioned applications. Poor reporting in these studies challenges the analysis process, so it is recommended that future studies report comprehensive results based on different metrics.

Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study

This paper presents a random walk approach to the problem of querying informative constraints for clustering. The proposed method is based on the properties of the commute time, that is the expected time taken for a random walk to travel between two nodes and return, on the adjacency graph of data. Commute time has the nice property of that, the more short paths connect two given nodes in a graph, the more similar those nodes are. Since computing the commute time takes the Laplacian eigenspectrum into account, we use this property in a recursive fashion to query informative constraints for clustering. At each recursion, the proposed method constructs the adjacency graph of data and utilizes the spectral properties of the commute time matrix to bipartition the adjacency graph. Thereafter, the proposed method benefits from the commute times distance on graph to query informative constraints between partitions. This process iterates for each partition until the stop condition becomes true. Experiments on real-world data show the efficiency of the proposed method for constraints selection.

A Random Walk Approach to Query Informative Constraints for Clustering

During the last years, constrained clustering has emerged as an interesting direction in machine learning research With constrained clustering, the quality of results can be improved by using constraints if a high-quality set of constraints is selected Querying beneficial constraints is a challenging task because there is no metric for measuring the quality of constraints before clustering A new method is proposed in this study that estimates density and impurity of data points on different adjacency distances and calculates centrality for each data point by applying a density tracking approach on the obtained densities The obtained information is then used to select a set of high-quality constraints Multi-resolution density analysis to more accurately estimate the point-point relationship of data, data density tracking in order to estimate the impurity and centrality of data, and selection of constraints from skeleton of clusters in order to discover the intrinsic structure of data can be mentioned as the most important contributions of this study To verify the effectiveness of the proposed method, we conducted a series of experiments on real data sets The obtained results show that the proposed algorithm can improve the clustering process compare with some recent reference algorithms

A Density-based Approach for Querying Informative Constraints for Clustering

This paper presents a new segmentation method for color images. It relies on soft and hard segmentation processes. In the soft segmentation process, a cellular learning automata analyzes the input image and closes together the pixels that are enclosed in each region to generate a soft segmented image. Adjacency and texture information are encountered in the soft segmentation stage. Soft segmented image is then fed to the hard segmentation process to generate the final segmentation result. As the proposed method is based on CLA it can adapt to its environment after some iterations. This adaptive behavior leads to a semi content-based segmentation process that performs well even in presence of noise. Experimental results show the effectiveness of the proposed segmentation method.

Ahmad Ali Abin

Papers

Real-time multiple face detection and tracking

Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study

A Random Walk Approach to Query Informative Constraints for Clustering

A Density-based Approach for Querying Informative Constraints for Clustering

Cellular learning automata-based color image segmentation using adaptive chains