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1,969 results on '"Lv, Lin"'

1. Using Machine Learning Technologies in Pressure Injury Management: Systematic Review

Author

Jiang, Mengyao, Ma, Yuxia, Guo, Siyi, Jin, Liuqi, Lv, Lin, Han, Lin, and An, Ning

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundPressure injury (PI) is a common and preventable problem, yet it is a challenge for at least two reasons. First, the nurse shortage is a worldwide phenomenon. Second, the majority of nurses have insufficient PI-related knowledge. Machine learning (ML) technologies can contribute to lessening the burden on medical staff by improving the prognosis and diagnostic accuracy of PI. To the best of our knowledge, there is no existing systematic review that evaluates how the current ML technologies are being used in PI management. ObjectiveThe objective of this review was to synthesize and evaluate the literature regarding the use of ML technologies in PI management, and identify their strengths and weaknesses, as well as to identify improvement opportunities for future research and practice. MethodsWe conducted an extensive search on PubMed, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, China National Knowledge Infrastructure (CNKI), the Wanfang database, the VIP database, and the China Biomedical Literature Database (CBM) to identify relevant articles. Searches were performed in June 2020. Two independent investigators conducted study selection, data extraction, and quality appraisal. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). ResultsA total of 32 articles met the inclusion criteria. Twelve of those articles (38%) reported using ML technologies to develop predictive models to identify risk factors, 11 (34%) reported using them in posture detection and recognition, and 9 (28%) reported using them in image analysis for tissue classification and measurement of PI wounds. These articles presented various algorithms and measured outcomes. The overall risk of bias was judged as high. ConclusionsThere is an array of emerging ML technologies being used in PI management, and their results in the laboratory show great promise. Future research should apply these technologies on a large scale with clinical data to further verify and improve their effectiveness, as well as to improve the methodological quality.

Published
2021
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8. More Teachers Make Greater Students: Compression of CycleGAN

Author

Liu, Xiaoxi, Lv, Lin, Liu, Ju, Han, Yanyang, Liang, Mengnan, Jiang, Xiao, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Shi, Zhongzhi, editor, Torresen, Jim, editor, and Yang, Shengxiang, editor

Published
2024
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11. CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

Author

Lv, Jinkai, Hu, Yuyong, Fu, Quanshui, Zhang, Zhiwang, Hu, Yuqiang, Lv, Lin, Yang, Guoqing, Li, Jinpeng, and Zhao, Yi

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multi-scale Feature Interaction) block and ACRE (Axial Context Relation Encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multi-scale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our in-house dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://github.com/ProgrammerHyy/CM-MLP.

Published
2022

12. A Literature Review on the Empirical Studies of the Integration of Mathematics and Computational Thinking

Author

Lv, Lin, Zhong, Baichang, and Liu, Xiaofan

Abstract

In K-12 education, Computational Thinking (CT) has been a trendy issue in mathematics education, but the approach and results of CT + Math are not yet clear enough. This paper systematically reviewed 22 SSCI journal papers from three perspectives: the current status, outcomes, and implications of mathematics and CT integration. Results indicate that: (1) The empirical studies were more inclined to be carried out in primary school; (2) The sample size inversely proportional to the duration, the same as the duration and the learning phase; (3) the integration of mathematics and CT were gradually emerging in kindergartens, while the empirical studies in junior and senior high schools still needs to be improved; (4) The experimental type prioritizes case studies and lacks mixed research; (5) Most research designs employ a variety of measuring instruments but limited in multimodal data; (6) Through the teaching model of plug-in programming, the integration of mathematics and CT was centred on the field of geometry and number operations; and (7) The CT skills involved are mainly Problem decomposition, Pattern recognition, Abstraction, Algorithm design and Debugging. The limitations and future directions are also discussed in this paper.

Published
2023
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28. Classification of Visual Field Abnormalities in Highly Myopic Eyes without Pathologic Change

Author

Lin, Fengbin, Chen, Shida, Song, Yunhe, Li, Fei, Wang, Wei, Zhao, Zhenni, Gao, Xinbo, Wang, Peiyuan, Jin, Ling, Liu, Yuhong, Chen, Meiling, Liang, Xiaohong, Yang, Bin, Ning, Guili, Cheng, Ching-Yu, Healey, Paul R, Park, Ki Ho, Zangwill, Linda M, Aung, Tin, Ohno-Matsui, Kyoko, Jonas, Jost B, Weinreb, Robert N, Zhang, Xiulan, Group, Glaucoma Suspects with High Myopia Study, Liu, Yizhi, Lv, Lin, Friedman, David, Gao, Kai, Liu, Bingqian, Bressler, Neil M, He, Mingguang, Chen, Xiang, and Tang, Guangxian

Subjects
Biomedical and Clinical Sciences, Ophthalmology and Optometry, Eye Disease and Disorders of Vision, Neurosciences, Aging, Clinical Research, Clinical Trials and Supportive Activities, Eye, Glaucoma, Humans, Intraocular Pressure, Longitudinal Studies, Myopia, Optic Disk, Retrospective Studies, Scotoma, Vision Disorders, Visual Field Tests, Visual Fields, Classification system, High myopia, Visual field abnormalities, Glaucoma Suspects with High Myopia Study Group, Clinical Sciences, Opthalmology and Optometry, Public Health and Health Services, Ophthalmology & Optometry, Ophthalmology and optometry
Abstract

PurposeTo develop a classification system of visual field (VF) abnormalities in highly myopic eyes with and without glaucoma.DesignSecondary analysis of VF data from a longitudinal cohort study.ParticipantsOne thousand eight hundred ninety-three VF tests from 1302 eyes (825 individuals).MethodsAll participants underwent VF testing (Humphrey 24-2 Swedish interactive threshold algorithm standard program; Carl Zeiss Meditec) and detailed ophthalmic examination. A comprehensive set of VF defect patterns was defined via observation of the 1893 VF reports, literature review, and consensus meetings. The classification system comprised 4 major types of VF patterns, including normal type, glaucoma-like defects (paracentral defect, nasal step, partial arcuate defect, arcuate defect), high myopia-related defects (enlarged blind spot, vertical step, partial peripheral rim, nonspecific defect), and combined defects (nasal step with enlarged blind spot). A subset (n = 1000) of the VFs was used to evaluate the interobserver and intraobserver agreement and weighted κ values of the classification system by 2 trained readers. The prevalence of various VF patterns and their associated factors were determined.Main outcome measuresThe classification of VF in highly myopic eyes and its associated risk factors.ResultsWe found that normal type, glaucoma-like defects, high myopia-related defects, and combined defects accounted for 74.1%, 10.8%, 15.0%, and 0.1% of all unique VF tests, respectively. The interobserver and intraobserver agreements were > 89%, and the corresponding κ values were 0.86 or more between readers. Both glaucoma-like and high myopia-related VF defects were associated with older age (odds ratios [ORs], 1.07 [95% confidence interval (CI), 1.04-1.10; P < 0.001] and 1.06 [95% CI, 1.04-1.10; P < 0.001]) and longer axial length (ORs, 1.65 [95% CI, 1.32-2.07; P < 0.001] and 1.37 [95% CI, 1.11-1.68; P = 0.003]). Longer axial length showed a stronger effect on the prevalence of glaucoma-like VF defects than on the prevalence of high myopia-related VF defects (P = 0.036).ConclusionsWe propose a new and reproducible classification system of VF abnormalities for nonpathologic high myopia. Applying a comprehensive classification system will facilitate communication and comparison of findings among studies.

Published
2022

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