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Bibliometric analysis of top papers in ecology based on essential science indicators during 2011–2021

Yuan Bao-Zhong, Sun Jie

Polish Journal of Ecology

2023

Vol. 71, nr 1

1--14

Based on the Essential Science Indicators database, this study analyzed 1,777 top papers in the Ecology subject category of Web of Science, for eleven years from 2011 to 2021, which included 1,770 highly cited papers and 15 hot papers in the field and belonged to 33 categories and 29 research areas. All top papers written in English came from 12,677 authors, 3,246 organizations and 123 countries or territories, and were published in 104 journals and 5 book series in the field. The top five journals publishing the highest number of top papers are Proceedings of the Royal Society B Biological Sciences (9.96% of papers), Global Change Biology (7.88%), ISME Journal (7.71%), Landscape and Urban Planning (7.54%) and Trends in Ecology and Evolution (5.01%), each published more than 89 papers. Top five countries were USA, England, Australia, Germany and Canada. Furthermore, top six organizations publishing the highest number of top papers are University of California, Berkeley, University of Oxford, Chinese Academy of Sciences, University of Queensland, University of British Columbia, and University of California, Davis (more than 62 papers each). VOSviewer software supported the bibliometric analysis. Co-occurrence analysis of top papers' keywords identified eight clusters that correspond to eight major research topics representing different viewpoints on Ecology. Those main topics are: ecosystem services and conservation management, climate-change impacts, evolution and selection, biodiversity, diversity and abundance, ecology patterns and community structure, ecology prediction, impacts of biological invasions. The subject of ecosystem services and conservation management is a front or recent interest topics in Ecology.

A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks

Li Jing, Wu Yurun, Shen Nanyan, Zhang Jiawen, Chen Enlong, Sun Jie, Deng Zongqian, Zhang Yuchen

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

238--248

The cancer of liver, which is the leading cause of cancer death, is commonly diagnosed by comparing the changes of gray level of liver tissue in the different phases of the patient's CT images. To aid the doctor in reducing misdiagnosis or missed diagnosis, a fully automatic computer-aided diagnosis (CAD) system is proposed to diagnose hepatocellular carcinoma (HCC) using convolutional neural network (CNN) classifier. The automatic segmentation and classification are two core technologies of the proposed CAD system, which are both realized based on CNN. The segmentation of liver and tumor is implemented by a fully convolutional networks (FCN) based on a fine tuning VGG-16 model with two additional 'skip structures' using a weighted loss function which helps to solve the problem of inaccurate tumor segmentation caused by the inevitably unbalanced training data. HCC classification is implemented by a 9-layer CNN classifier, whose input is a 4-channel image data constructed by combining the segmentation result of FCN with the original CT image. A total of 165 venous phase CT images including 46 diffuse tumors, 43 nodular tumors, and 76 massive tumors are used to evaluate the performance of the proposed CAD system. The classification accuracy of CNN classifier for diffuse, nodular and massive tumors are 98.4%, 99.7% and 98.7% respectively, which are significantly improved in contrast with the traditional feature-based ANN and SVM classifiers. The proposed CAD system, which is unaffected by the difference of preprocessing method and feature type, is proved satisfactory and feasible by the test set.