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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Tinghui Lu, Jui-Ting Lin, Beatrice Chang, Dawei Wang, Pochuan Wang, Mei-Chi Lee, Peijung Wang, Weichung National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan Department of Mathematics Ecole Normale Supérieure de Lyon Lyon France Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mathematics National Taiwan University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Artificial intelligence (ai) training courses often require prerequisites such as calculus or statistics. It is hence challenging to design and develop an introductory ai course for students of secondary education. This research intends to develop a medical ai course, provide high school students with an overview of deep learning applications in medical image analysis, and inspire them to pursue careers in the field of medical ai. We designed a 20-hour course, including lectures and two hands-on projects based on medical image classification. The proposed courses provided medical ai disciplines and built up their knowledge from basic to advanced levels. During the ten-day online courses, all the students were fully engaged and gave us positive feedback. The students endeavored to complete the experimental study in training, testing, and hypothesis of medical images application in the course. Their performance exceeded all expectations, for they did further analysis by tuning different hyperparameters. We designed a course evaluation form, which suggested that the students found it essential and expected to interact with the instructors. The results indicate that combining lectures with hands-on sessions would lead to evidently better achievement in terms of high school students' medical ai knowledge and positive attitudes while addressing real-world problems in the projects. Through this innovative education model, high school students regained their enthusiasm and were encouraged to cultivate their medical ai skills through self-learning while finishing the project. We conclude that this course could be successfully applied to interdisciplinary education in high school. © 2021 SPIE.

关键词： Students

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V³H: View Variation and View Heredity for Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2020年第3期1卷 233-247页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Oliver The School of Computer Science and Technology Key Laboratory of Information Storage System Ministry of Education of China Huazhong University of Science and Technology Wuhan 430074 China The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China The Department of Electrical and Computer Engineering University of Florida Gainesville 32611 FL United States

Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V³H). Inspired by the variation and the heredity in genetics, V³H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V³H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V³H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V³H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multiview data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts. Impact Statement—Incomplete multiview clustering is a popular technology to cluster incomplete datasets from multiple sources. The technology is becoming more significant due to the absence of the expensive requirement of labeling these datasets. However, previous algorithms cannot fully learn the information of each view. Inspired by variation and heredity in genetics, our proposed algorithm V³H fully learns the information of each view. Compared with the state-of-the-art algorithms, V³H improves clustering

关键词： Incomplete multiview clustering view heredity view variation

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Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

arXiv

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arXiv 2023年

作者： Qiao, Yuanyuan Niu, Yong Han, Zhu Mao, Shiwen He, Ruisi Wang, Ning Zhong, Zhangdui ai, Bo The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable Intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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Exploratory study of highly susceptible to SARS-CoV-2 populations

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Life research 2022年第3期5卷 10-14页

作者： Xiaodong Jia Chengliang Yin Shanshan Lu Yan Chen Qingyan Liu Junfan Bai Lei Zhi Yinying Lu Comprehensive Liver Cancer Center The Fifth Medical Center of PLA General HospitalBeijingChina.0000-0001-8262-5749 National Engineering Laboratory for Medical Big Data Application Technology Chinese PLA General HospitalBeijingChina Faculty of Medicine Macao University of Science and TechnologyMacaoChina Medical Big Data Research Center Medical Innovation Research Division of PLA General HospitalBeijingChina Center for Synthetic and Systems Biology(CSSB) Tsinghua UniversityBeijingChina Peking University BeijingChina Department of Chronic Disease Luzhou Center for Disease Control and PreventionLuzhouChina

The coronavirus disease 2019(COVID-19),caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),is threatening public *** human angiotensin-converting enzyme 2(ACE2)has a remarkably high affinity binding to *** study explored whether certain populations,including obese and cancer patients,are susceptible to *** expression pattern of ACE2 in normal and tumor tissues of cancer patients was compared by the search for network database and re-analysis of pubic *** level of ACE2 expression in normal adipose tissue was higher than that in normal lung tissue,which indicated the adipose tissue might be vulnerable to SARS-CoV-2;the levels of ACE2 expressed by adipocytes and adipose progenitor cells were similar between non-obese individuals and obese individuals(BMI>29),but obese individuals have more adiposes so as to increase the number of ACE2-expressing cells;the expression of ACE2 in tumor tissues posed by five different types of cancers increased significantly compared with that in adjacent ***,we proposed the following hypothesis:the obese individuals and five types of cancer patients might have a higher risk of SARS-CoV-2 infection,which might become the target population of SARS-CoV-2 infection in the future.

关键词： COVID-19 adipose tissue obesity cancer ACE2

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可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

引用

science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

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Learning from noisy labels via dynamic loss thresholding

arXiv

引用

arXiv 2021年

作者： Yang, Hao Jin, Youzhi Li, Ziyin Wang, Deng-Bao Miao, Lei Geng, Xin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China AI Technology Application Department Huawei Technologies Collaborative Innovation Center of Wireless Communications Technology China

Numerous researches have proved that deep neural networks (DNNs) can fit everything in the end even given data with noisy labels, and result in poor generalization performance. However, recent studies suggest that DNNs tend to gradually memorize the data, moving from correct data to mislabeled data. Inspired by this finding, we propose a novel method named Dynamic Loss Thresholding (DLT). During the training process, DLT records the loss value of each sample and calculates dynamic loss thresholds. Specifically, DLT compares the loss value of each sample with the current loss threshold. Samples with smaller losses can be considered as clean samples with higher probability and vice versa. Then, DLT discards the potentially corrupted labels and further leverages supervised learning techniques. Experiments on CIFAR-10/100 and Clothing1M demonstrate substantial improvements over recent state-of-the-art methods. In addition, we investigate two real-world problems for the first time. Firstly, we propose a novel approach to estimate the noise rates of datasets based on the loss difference between the early and late training stages of DNNs. Secondly, we explore the effect of hard samples (which are difficult to be distinguished) on the process of learning from noisy labels. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Knowledge discovery of patients reviews on breast cancer drugs: Segmentation of side effects using machine learning techniques

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Heliyon 2024年第19期10卷 e38563页

作者： Nilashi, Mehrbakhsh Ahmadi, Hossein Abumalloh, Rabab Ali Alrizq, Mesfer Alghamdi, Abdullah Alyami, Sultan UCSI Graduate Business School UCSI University Cheras Kuala Lumpur 56000 Malaysia Centre for Health Technology Faculty of Health University of Plymouth Plymouth PL4 8AA United Kingdom Department of Computer Science and Engineering Qatar University Doha 2713 Qatar Information Systems Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Dept. College of Computer Science and Information Systems Najran University Najran Saudi Arabia AI Lab Scientific and Engineering Research Center (SERC) Najran University Najran Saudi Arabia Centre for Business Informatics and Industrial Management UCSI Graduate Business School UCSI University Malaysia

Breast cancer stands as the most frequently diagnosed life-threatening cancer among women worldwide. Understanding patients' drug experiences is essential to improving treatment strategies and outcomes. In this research, we conduct knowledge discovery on breast cancer drugs using patients' reviews. A new machine learning approach is developed by employing clustering, text mining and regression techniques. We first use Latent Dirichlet Allocation (LDA) technique to discover the main aspects of patients' experiences from the patients' reviews on breast cancer drugs. We also use Expectation-Maximization (EM) algorithm to segment the data based on patients' overall satisfaction. We then use the Forward Entry Regression technique to find the relationship between aspects of patients' experiences and drug's effectiveness in each segment. The textual reviews analysis on breast cancer drugs found 8 main side effects: Musculoskeletal Effects, Menopausal Effects, Dermatological Effects, Metabolic Effects, Gastrointestinal Effects, Neurological and Cognitive Effects, Respiratory Effects and Cardiovascular. The results are provided and discussed. The findings of this study are expected to offer valuable insights and practical guidance for prospective patients, aiding them in making informed decisions regarding breast cancer drug consumption. © 2024 The Authors

关键词： Breast cancer Drugs Knowledge discovery Machine learning Online reviews Public health Text mining

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Survey on deep learning in multimodal medical imaging for cancer detection

arXiv

引用

arXiv 2023年

作者： Tian, Yan Xu, Zhaocheng Ma, Yujun Ding, Weiping Wang, Ruili Gao, Zhihong Cheng, Guohua He, Linyang Zhao, Xuran School of Computer Science and Technology Zhejiang Gongshang University Hangzhou310018 China School of Mathematical and Computational Sciences Massey University Auckland0632 New Zealand School of Information Science and Technology Nantong University Nantong226019 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou325000 China Zhejiang Engineering Research Center of Intelligent Medicine Wenzhou325000 China Jianpei Technology Co. Ltd Hangzhou311200 China

The task of multimodal cancer detection is to determine the locations and categories of lesions by using different imaging techniques, which is one of the key research methods for cancer diagnosis. Recently, deep learning-based object detection has made significant developments due to its strength in semantic feature extraction and nonlinear function fitting. However, multimodal cancer detection remains challenging due to morphological differences in lesions, interpatient variability, difficulty in annotation, and imaging artifacts. In this survey, we mainly investigate over 150 papers in recent years with respect to multimodal cancer detection using deep learning, with a focus on datasets and solutions to various challenges such as data annotation, variance between classes, small-scale lesions, and occlusion. We also provide an overview of the advantages and drawbacks of each approach. Finally, we discuss the current scope of work and provide directions for the future development of multimodal cancer detection. Copyright © 2023, The Authors. All rights reserved.

关键词： computer vision

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Accurately predicting anticancer peptide using an ensemble of heterogeneously trained classifiers

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Informatics in Medicine Unlocked 2023年 42卷

作者： Azim, Sayed Mehedi Sabab, Noor Hossain Nuri Noshadi, Iman Alinejad-Rokny, Hamid Sharma, Alok Shatabda, Swakkhar Dehzangi, Iman Center for Computational and Integrative Biology Rutgers University Camden 08102 NJ United States Department of Computer Science and Engineering United International University Plot 2 United City Madani Avenue BaddaDhaka 1212 Bangladesh Department of Bioengineering University of California Riverside 92507 CA United States BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering The University of New South Wales (UNSW Sydney) Sydney NSW 2052 Australia UNSW Data Science Hub UNSW Sydney Sydney NSW 2052 Australia Health Data Analytics Program AI-enabled Processes Research Centre Macquarie University Sydney 2109 Australia Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Laboratory for Medical Science Mathematics RIKEN Center for Integrative Medical Sciences Yokohama 230-0045 Japan Department of Computer Science Rutgers University Camden 08102 NJ United States

The use of therapeutic peptides for the treatment of cancer has received tremendous attention in recent years. Anticancer peptides (ACPs) are considered new anticancer drugs which have several advantages over chemistry-based drugs including high specificity, strong tumor penetration capacity, and low toxicity level for normal cells. Due to the rise of experimentally verified bioactive peptides, several in silico approaches became imperative for the investigation of the characteristics of ACPs. In this paper, we proposed a new machine learning tool named iACP-RF that uses a combination of several sequence-based features and an ensemble of three heterogeneously trained Random Forest classifiers to accurately predict anticancer peptides. Experimental results show that our proposed model achieves an accuracy of 75.9% which outperforms other state-of-the-art methods by a significant margin. We also achieve 0.52, 75.6%, and 76.2% in terms of Matthews Correlation Coefficient (MCC), Sensitivity, and Specificity, respectively. iACP-RF as a standalone tool and its source code are publicly available at: https://***/MLBC-lab/iACP-RF. © 2023 The Authors

关键词： Anticancer peptides Ensemble learning Feature extraction Heterogeneous classifiers Machine learning Random Forest

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Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics

引用

Neuroimage: Reports 2023年第4期3卷 100186-100186页

作者： Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University Atlanta GA United States Department of Computer Science Georgia State University Atlanta GA United States

Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies. © 2023

关键词： Clustering Deep learning Dynamic functional network connectivity Explainable ai Resting state functional magnetic resonance imaging Schizophrenia

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