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Current trends of artificial intelligence in cancer imaging

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artificial intelligence in Medical Imaging 2020年第3期1卷 87-93页

作者： Francesco Verde Valeria Romeo Arnaldo Stanzione Simone Maurea department of Advanced Biomedical sciences University of Naples"Federico II"Napoli 80131Italy

In this editorial,we discussed the current research status of artificial intelligence(AI)in Oncology,reviewing the basics of machine learning(ML)and deep learning(DL)techniques and their emerging applications on clinical and imaging cancer *** growing amounts of available“big data”coupled to the increasing computational power have enabled the development of computerbased systems capable to perform advanced tasks in many areas of clinical care,especially in medical *** is a branch of data science that allows the creation of computer algorithms that can learn and make predictions without prior *** is a subgroup of artificial neural network algorithms configurated to automatically extract features and perform high-level tasks;convolutional neural networks are the most common DL models used in medical image *** methods have been proposed in many areas of oncology granting promising results in radiology-based clinical *** detail,we explored the emerging applications of AI in oncological risk assessment,lesion detection,characterization,staging,and therapy *** issues such as the lack of reproducibility and generalizability need to be addressed to fully implement AI systems in clinical ***,AI impact on cancer imaging has been driving the shift of oncology towards a precision diagnostics and personalized cancer treatment.

关键词： artificial intelligence Machine learning Deep learning Oncology Medical imaging Cancer imaging

Meta-Semi:A Meta-Learning Approach for Semi-Supervised Learning

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CAAI artificial intelligence Research 2022年第2期1卷 161-171页

作者： Yulin Wang Jiayi Guo Jiangshan Wang Cheng Wu Shiji Song Gao Huang department of Automation BNRistTsinghua UniversityBeijing 100084China Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518131China

Deep learning based semi-supervised learning(SSL)algorithms have led to promising results in recent ***,they tend to introduce multiple tunable hyper-parameters,making them less practical in real SSL scenarios where the labeled data is scarce for extensive hyper-parameter *** this paper,we propose a novel meta-learning based SSL algorithm(Meta-Semi)that requires tuning only one additional hyper-parameter,compared with a standard supervised deep learning algorithm,to achieve competitive performance under various conditions of *** start by defining a meta optimization problem that minimizes the loss on labeled data through dynamically reweighting the loss on unlabeled samples,which are associated with soft pseudo labels during *** the meta problem is computationally intensive to solve directly,we propose an efficient algorithm to dynamically obtain the approximate *** show theoretically that Meta-Semi converges to the stationary point of the loss function on labeled data under mild ***,Meta-Semi outperforms state-of-the-art SSL algorithms significantly on the challenging semi-supervised CIFAR-100 and STL-10 tasks,and achieves competitive performance on CIFAR-10 and SVHN.

关键词： deep learning semi-supervised learning computer vision

Application of artificial intelligence in hepatology:Minireview

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artificial intelligence in Gastroenterology 2020年第1期1卷 5-11页

作者： Ryota Masuzaki Tatsuo Kanda Reina Sasaki Naoki Matsumoto Kazushige Nirei Masahiro Ogawa Mitsuhiko Moriyama Division of Gastroenterology and Hepatology Department of MedicineNihon University School of MedicineTokyo 173-8610Japan

With the rapid advancements in computer science,artificial intelligence(AI)has become an intrinsic part of our daily life and clinical *** concepts of AI,such as machine learning,deep learning,and big data,are extensively used in clinical and basic *** this review,we searched for the articles in PubMed and summarized recent developments of AI concerning hepatology while focusing on the diagnosis and risk assessment of liver *** is widely conducted for the routine surveillance of hepatocellular carcinoma along with tumor ***-aided diagnosis is useful in the detection of tumors and characterization of space-occupying *** prognosis of hepatocellular carcinoma can be estimated via AI using large-scale and highquality training *** prevalence of nonalcoholic fatty liver disease is increasing worldwide and pivotal concern in the field is who will progress and develop hepatocellular *** AI studies require a large dataset,including laboratory or radiological findings and outcome *** will be useful in reducing medical errors,supporting clinical decisions,and predicting clinical ***,cooperation between AI and humans is expected to improve healthcare.

关键词： artificial intelligence Deep learning Machine learning Hepatocellular carcinoma Prognosis computer-aided diagnosis

Application of artificial intelligence in liver diseases: From diagnosis to treatment

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artificial intelligence in Gastroenterology 2021年第5期2卷 133-140页

作者： Qiong Li Jun-Feng Li Xiao-Rong Mao The First Clinical Medical College Lanzhou UniversityLanzhou 730000Gansu ProvinceChina department of Infectious Diseases&Institute of Infectious Diseases the First Hospital of Lanzhou UniversityLanzhou 730000Gansu ProvinceChina department of Infectious Diseases The First Hospital of Lanzhou UniversityLanzhou 730000Gansu ProvinceChina

Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease *** were an estimated 2.14 million liver-related deaths in 2017,representing an 11.4%increase since *** diagnosis and treatment methods have various dilemmas in different causes of liver *** a hot research topic in recent years,the application of artificial intelligence(AI)in different fields has attracted extensive attention,and new technologies have brought more ideas for the diagnosis and treatment of some liver *** learning(ML)is the core of AI and the basic way to make a computer *** technology has many potential uses in hepatology,ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image *** application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly,accurately and scientifically,which is of importance for reducing the incidence and mortality of liver diseases,reducing medical errors,and promoting the development of *** paper reviews the application and prospects of AI in liver diseases,and aims to improve clinicians’awareness of the importance of AI in the diagnosis and treatment of liver diseases.

关键词： artificial intelligence Machine learning Liver disease Diagnosis Treatment Prognosis

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Journal of computer science Research 2023年第3期5卷 1-14页

作者： Zhaohao Sun department of Business Studies PNG University of TechnologyPrivate Mail BagLae 411MorobePapua New Guinea

Similarity has been playing an important role in computer science,artificial intelligence(AI)and data ***,similarity intelligence has been ignored in these *** intelligence is a process of discovering intelligence through *** article will explore similarity intelligence,similarity-based reasoning,similarity computing and *** specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real *** explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data *** article explores similarity-based reasoning(SBR)and proposes three similarity-based inference *** then examines similarity computing and analytics,and a multiagent SBR *** main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity *** proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning.

关键词： Similarity intelligence Similarity computing Similarity analytics Similarity-based reasoning Big data analytics artificial intelligence Intelligent agents

Research on Text Mining of Syndrome Element Syndrome Differentiation by Natural Language Processing

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Digital Chinese Medicine 2019年第2期2卷 61-71页

作者： DENG Wen-Xiang ZHU Jian-Ping LI Jing YUAN Zhi-Ying WU Hua-Ying YAO Zhong-Hua ZHANG Yi-Ge ZHANG Wen-An HUANG Hui-Yong Hunan university of Chinese Medicine ChangshaHunan 410208China TCM Diagnostic Institute Hunan University of Chinese MedicineChangshaHunan 410208China Administration of Traditional Chinese Medicine of Hunan Province ChangshaHunan 410008China Guangzhou Jiayibang Health Management Co. Ltd.GuangzhouGuangdong 510000China

Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.

关键词： Syndrome element syndrome differentiation (SESD) Natural language processing (NLP) Diagnostics of TCM artificial intelligence Text mining

Machine intelligence for precision oncology

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World Journal of Translational Medicine 2021年第1期9卷 1-10页

作者： Nelson S Yee department of Medicine The Pennsylvania State University College of MedicinePenn State Cancer InstitutePenn State Health Milton S.Hershey Medical CenterHersheyPA 17033-0850United States

Despite various advances in cancer research,the incidence and mortality rates of malignant diseases have remained *** risk assessment,prevention,detection,and treatment of cancer tailored to the individual are major challenges in clinical *** intelligence(AI),a field of applied computer science,has shown promising potential of accelerating evolution of healthcare towards precision *** article focuses on highlights of the application of data-driven machine learning(ML)and deep learning(DL)in translational research for cancer diagnosis,prognosis,treatment,and clinical *** algorithms in radiological and histological images have been demonstrated to improve detection and diagnosis of ***-based prediction models in molecular or multi-omics datasets of cancer for biomarkers and targets enable drug discovery and *** approaches combining radiomics with genomics and other omics data enhance the power of AI in improving diagnosis,prognostication,and treatment of *** and regulatory issues involving patient confidentiality and data security impose certain limitations on practical implementation of ML in clinical ***,the ultimate goal of application of AI in cancer research is to develop and implement multi-modal machine intelligence for improving clinical decision on individualized management of patients.

关键词： artificial intelligence Deep learning Machine learning Precision oncology Radiomics Radiogenomics

把握机会窗口期减缓气候变化对中国居民健康影响

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科学通报 2023年第15期68卷 1899-1905页

作者：蔡闻佳张弛张诗卉艾思奇白玉琪鲍俊哲常楠陈彬陈慧琪程亮亮崔学勤戴瀚程 Bawuerjiang Danna 底骞董伟董文轩窦德景范维澄范星房小怡高仝高源耿阳关大博郭亚菲 Ian Hamilton 胡艺馨华峻翊黄存瑞黄弘黄建斌蒋俏蕾姜晓朋柯丕煜 Gregor Kiesewetter Pete Lampard 李传玺李瑞奇李双利梁璐林波荣林华亮刘欢刘起勇刘小波刘心远刘昱甫刘钊刘竹楼书含鲁晨曦罗勇罗震宇马伟 Alice McGushin 牛彦麟任超阮增良 Wolfgang Schöpp 单钰理苏婧孙韬淳王灿王琼文三妹谢杨熊辉徐冰徐朦颜钰杨军杨廉平杨秀俞乐岳玉娟曾仪娉张镜张少辉张曜张仲宸赵继尧赵亮赵梦真赵琦赵哲周景博朱征宏陈冯富珍宫鹏清华大学地球系统科学系北京100084 北京理工大学管理与经济学院北京100081 中山大学公共卫生学院广州510080 郑州大学公共卫生学院郑州450001 南京医科大学公共卫生学院南京211166 北京师范大学环境学院北京100875 北京大学环境工程学院北京100871 清华大学新闻与传播学院北京100084 清华大学万科公共卫生学院北京100084 浙江大学地球科学学院杭州310058 清华大学公共安全研究所北京100084 清华大学工程物理系北京100084 百度研究院北京100091 山东师范大学环境与生态研究院济南250013 中国气象科学研究院气象影响与风险研究中心北京100086 山东师范大学商学院济南250013 中国疾病预防控制中心传染病预防控制所传染病预防控制国家重点实验室北京102206 清华大学建筑学院北京100084 中国疾病预防控制中心环境与人群健康重点实验室环境与健康相关产品安全所北京102206 Energy Institute University College LondonLondon WC1E 6BTUK 南方科技大学统计与数据科学系深圳518055 香港大学建筑学院中国香港999077 世界卫生组织驻华代表处北京100600 Pollution Management Research Group EnergyClimateand Environment ProgramInternational Institute for Applied Systems Analysis(IIASA)Schlossplatz 1-A-2361 LaxenburgAustria department of Health sciences University of YorkYork YO105DDCanada 山东大学齐鲁医学院公共卫生学院流行病学系济南250002 中国科学技术大学计算机科学与技术学院合肥230027 department of Geography and the Environment University of North TexasDenton TX 76203-5017USA 清华大学环境学院北京100084 Geography College of Life and Environmental SciencesUniversity of ExeterExeter EX12LUUK 山东大学气候变化与健康研究中心济南250002 Institute for Global Health University College LondonLondon WC1H 0ALUK School of Geography Earth and Environmental SciencesUniversity of BirminghamBirmingham B152TTUK 清华大学人文学院北京100084 北京航空航天大学经济与管理学院北京100083 香港科技大学(广州)信息枢纽人工智能学域广州511443 暨南大学环境与气候研究所广州510632 清华大学气候变化与可持续发展研究院北京100084 清华大学苏世民学院北京100084 清华大学体育学部北京100084 中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室北京100029 香港大学地球科学系和地理系中国香港999077

随着中国总人口的不断增加以及改革开放以来经济的飞速发展,中国面临来自气候变化的健康威胁也在不断上升.与此同时,中国也处于一个独特的机遇窗口期,如果能够有效应对气候变化带来的健康风险,将造福今后几代人的健康.反之,如果没有及... 详细信息

随着中国总人口的不断增加以及改革开放以来经济的飞速发展,中国面临来自气候变化的健康威胁也在不断上升.与此同时,中国也处于一个独特的机遇窗口期,如果能够有效应对气候变化带来的健康风险,将造福今后几代人的健康.反之,如果没有及时、充分的应对措施,气候变化对中国居民健康和生命的威胁将与日俱增.为了推动更及时且有利于改善人群健康的气候应对行动,由清华大学牵头建立的柳叶刀倒计时亚洲中心在全球柳叶刀倒计时工作的基础上[1,2],从2020年开始,聚焦气候变化对中国人群健康的影响进行全面且系统的评估[3,4].

关键词：减缓气候变化人群健康柳叶刀居民健康窗口期倒计时应对措施把握机会