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Artificial Intelligence for Dementia Research Methods Optimization

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arXiv 2023年

作者： Bucholc, Magda James, Charlotte Al Khleifat, Ahmad Badhwar, AmanPreet Clarke, Natasha Dehsarvi, Amir Madan, Christopher R. Marzi, Sarah J. Shand, Cameron Schilder, Brian M. Tamburin, Stefano Tantiangco, Hanz M. Lourida, Ilianna Llewellyn, David J. Ranson, Janice M. Cognitive Analytics Research Lab School of Computing Engineering & Intelligent Systems Ulster University Derry United Kingdom NIHR Bristol Biomedical Research Centre University Hospitals Bristol Weston NHS Foundation Trust University of Bristol Bristol United Kingdom Department of Basic and Clinical Neuroscience Institute of Psychiatry Psychology & Neuroscience King's College London London United Kingdom Lab Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal Montréal Canada Institut de Génie Biomédical Université de Montréal Montréal Canada Département de Pharmacologie et Physiologie Université de Montréal Montréal Canada Aberdeen Biomedical Imaging Centre School of Medicine Medical Sciences and Nutrition University of Aberdeen Aberdeen United Kingdom School of Psychology University of Nottingham Nottingham United Kingdom UK Dementia Research Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Department of Neurosciences Biomedicine and Movement Sciences University of Verona Verona Italy Information School University of Sheffield Sheffield United Kingdom University of Exeter Medical School Exeter United Kingdom The Alan Turing Institute London United Kingdom

Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. Methods: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. Results: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. Discussion: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia. © 2023, CC BY.

关键词： Clinical research

Toward evaluating the level of crowd wisdom using interval estimates

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Toward evaluating the level of crowd wisdom using interval e...

作者： Nguyen, Van Du Truong, Hai Bang Merayo, Mercedes G. Nguyen, Ngoc Thanh Division of Knowledge and System Engineering for ICT Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Viet Nam Department Sistemas Informáticos y Computación Universidad Complutense de Madrid Spain Faculty of Computer Science and Management Wroclaw University of Science and Technology Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh city Viet Nam

Recently, the use of the wisdom of crowds (WoC) for finding solutions to a wide range of real-life problems has dramatically expanded. Prior studies have revealed that diversity, independence, decentralization, and aggregation are the determinants of collective wisdom. However, these findings are often based on the so-called point estimates - single values are used as the representations of individual predictions on the task of estimating unknown quantities or predicting outcomes of future events. In some situations, interval values, which are often called interval estimates, can be used for such representations. Accordingly, one can provide an individual prediction in the form of an interval value including a lower and an upper bounds. Taking into account this kind of representation, in this paper, we present a case study in which collectives of randomly selected predictions can outperform those of most accurate predictions. Then, we evaluate the WoC level by taking into account diversity and cardinality. The computational experiments have indicated that diversity is positively related to collective wisdom. Finally, we discuss some related theoretical and practical implications for further research. © 2019 - IOS Press and the authors. All rights reserved.

关键词： Forecasting

Selective kernel networks

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arXiv 2019年

作者： Li, Xiang Wang, Wenhai Hu, Xiaolin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Momenta National Key Lab for Novel Software Technology Nanjing University Department of Computer Science and Technology Tsinghua University China

In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://***/implus/SKNet. Copyright © 2019, The Authors. All rights reserved.

关键词： Neurons

Shape robust text detection with progressive scale expansion network

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arXiv 2019年

作者： Wang, Wenhai Xie, Enze Li, Xiang Hou, Wenbo Lu, Tong Yu, Gang Shao, Shuai National Key Lab for Novel Software Technology Nanjing University Department of Comuter Science and Technology Tongji University School of Computer and Engineering Nanjing University of Science and Technology Momenta Technology Inc PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Scene text detection has witnessed rapid progress especially with the recent development of convolutional neural networks. However, there still exists two challenges which prevent the algorithm into industry applications. On the one hand, most of the state-of-art algorithms require quadrangle bounding box which is in-accurate to locate the texts with arbitrary shape. On the other hand, two text instances which are close to each other may lead to a false detection which covers both instances. Traditionally, the segmentation-based approach can relieve the first problem but usually fail to solve the second challenge. To address these two challenges, in this paper, we propose a novel Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes. More specifically, PSENet generates the different scale of kernels for each text instance, and gradually expands the minimal scale kernel to the text instance with the complete shape. Due to the fact that there are large geometrical margins among the minimal scale kernels, our method is effective to split the close text instances, making it easier to use segmentation-based methods to detect arbitrary-shaped text instances. Extensive experiments on CTW1500, Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of PSENet. Notably, on CTW1500, a dataset full of long curve texts, PSENet achieves a F-measure of 74.3% at 27 FPS, and our best F-measure (82.2%) outperforms state-of-art algorithms by 6.6%. The code will be released in the future. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical detection

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

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arXiv 2020年

作者： Fu, Huazhu Li, Fei Sun, Xu Cao, Xingxing Liao, Jingan Orlando, José Ignacio Tao, Xing Li, Yuexiang Zhang, Shihao Tan, Mingkui Yuan, Chenglang Bian, Cheng Xie, Ruitao Li, Jiongcheng Li, Xiaomeng Wang, Jing Geng, Le Li, Panming Hao, Huaying Liu, Jiang Kong, Yan Ren, Yongyong Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu State Key Laboratory Zhongshan Ophthalmic Center Sun Yat-sen University Guangzhou510060 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Intelligent Healthcare Unit Baidu Beijing China School of Computer Science and Engineering South China University of Technology GuangzhouGuangdong China National Scientific and Technical Research Council CONICET Argentina Tandil Argentina School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Tencent Jarvis Lab Shenzhen China School of Software Engineering South China University of Technology Guangzhou China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China School of Electronic and Information Engineering Shenzhen University Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong School of Electronic and Information Engineering Soochow University Suzhou China Ningbo University Zhejiang China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Zhejiang China Southern University of Science and Technology Shenzhen China Shanghai Jiaotong University Shanghai China Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University of Vienna Vienna Austria

Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually leads to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a uniform way, which limits the progress in the development of automated techniques for angle closure detection and assessment. To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019. The AGE challenge consisted of two tasks: scleral spur localization and angle closure classification. For this challenge, we released a large data of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models. During the AGE challenge, over 200 teams registered online, and more than 1100 results were submitted for online evaluation. Finally, eight teams participated in the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results in the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach had an average Euclidean Distance of 10 pixel (10µm) in scleral spur localization, while in the task of angle closure classification, all the algorithms achieved the satisfactory performances, especially, 100% accuracy rate for top-two performances. These artificial intelligence techniques were shown to have the potential to enable new developments in AS-OCT

关键词： Ophthalmology

A survey of gene selection and classification techniques based on cancer microarray data analysis 4

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A survey of gene selection and classification techniques bas...

4th IEEE International Conference on computer and Communications, ICCC 2018

作者： Zhang, Xinman He, Tingting Ouyang, Liwei Xu, Xuebin Chen, Sushing School of Electronics and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an China Guangdong xi'An Jiaotong University Academy Foshan China CISE Department for Computer Information Science Engineering University of Florida Systems Biology Lab Gainesville United States

ISBN: (纸本)9781538683392

A growing number of cancer deaths draws attractions in the areas of bioinformatics worldwide. It is worth noting that cancer microarray data is composed of tens of thousands of genes, but there are only a few dozens of genes that are really relative to cancers. Thus, it is particularly import to pick out the relevant genes by means of microarray data analysis. Gene selection and classification techniques capture the essence of cancer mechanism and represent a suite of the refreshing cancer cure. In the paper, we summarize 4 types of cancer microarrays which show up in reference providing their attributes and download websites. Furthermore, we also review most extensively studied gene selection and classification techniques. Meanwhile, comparison and analysis on accuracies of gene selection and classification methods in view of remarkably influential algorithms are outlined with unique and randomized algorithms. Thus, the most correlative cancer genes are selected, Leukemia and Colon cancer as typical representatives are listed in the survey. From the microcosmic perspectives, these emerging techniques are considerably conductive to solve the dimensional curse of cancer microarray data applied in the treatment of cancer. © 2018 IEEE.

关键词： Genes

Ontology based global and collective motion patterns for event classification in basketball videos

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arXiv 2019年

作者： Wu, Lifang Yang, Zhou He, Jiaoyu Jian, Meng Xu, Yaowen Xu, Dezhong Chen, Chang Wen College of Information and Communication Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Municipal Key Lab of Computation Intelligence and Intelligent Systems Beijing University of Technology Beijing100124 China School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Department of Computer Science and Engineering SUNY Buffalo NY United States

In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collective motion. In broadcast basketball videos, specific camera motions are used to present specific events. Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion. A semantic event in basketball videos can be generally divided into three stages: pre-event, event occurrence (event-occ), and post-event. By analyzing the influence of different stages of video segments to semantic events discrimination, it is observed that pre-event and event-occ segments are effective for classification, while post-events are effective for event success/failure classification. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for basketball event classification. First, a two-stage GCMP based event classification scheme is proposed. The GCMP is extracted using optical flow. The two-stage scheme progressively combines a five-class event classification algorithm on event-occs and a two-class event classification algorithm on pre-events. Both algorithms utilize sequential convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize post-event segments to predict success/failure using deep features of images in the video frames (RGB_DF_VF) based algorithms. Finally the event classification results and success/failure classification results are integrated to obtain the final results. To evaluate the proposed scheme, we collected a new dataset called NCAA+, which is automatically obtained from the NCAA dataset by extending the fixed length of video clips forward and backward of the corresponding semantic events. The experimental results demonstrate that the proposed

关键词： Sports

Reports of the AAAI 2019 spring symposium series

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Reports of the AAAI 2019 spring symposium series

作者： Baldini, Ioana Barrett, Clark Chella, Antonio Cinelli, Carlos Gamez, David Gilpin, Leilani H. Hinkelmann, Knut Holmes, Dylan Kido, Takashi Kocaoglu, Murat Lawless, William F. Lomuscio, Alessio Macbeth, Jamie C. Martin, Andreas Mittu, Ranjeev Patterson, Evan Sofge, Donald Tadepalli, Prasad Takadama, Keiki Wilson, Shomir IBM Research AI Department of Computer Science at Stanford University United States Department of Engineering University of Palermo Italy University of California Los Angeles United States Department of Computer Science Middlesex University London United Kingdom Massachusetts Institute of Technology United States FHNW University of Applied Sciences and Arts Northwestern Switzerland Switzerland Massachusetts Institute of Technology United States Visiting Researcher of Stanford University United States Researcher at MIT-IBM Watson AI Lab Paine College United States Department of Computing Imperial College London United Kingdom Smith College United States FHNW University of Applied Sciences and Arts Northwestern Switzerland Switzerland US Naval Research Laboratory Information Management and Decision Architectures Branch within Information Technology Division United States Department of Statistics at Stanford University United States Computer Scientist and Roboticist at US Naval Research Laboratory United States Computer Science at Oregon State University United States University of Electro Communications in Japan Japan College of Information Sciences and Technology at Pennsylvania State University United States

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data;self-driving cars have already been involved in fatalities, including a pedestrian;and yet machine learning is unable to explain the contexts within which it operates. Copyright © 2019, Association for the Advancement of Artificial Intelligence.

关键词： Machine learning

Correction to: Integrating Social Assistive Robots, IoT, Virtual Communities and Smart Objects to Assist at-Home Independently Living Elders: the MoveCare Project

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International Journal of Social Robotics 2022年第9期14卷 2045-2045页

作者： Luperto, Matteo Monroy, Javier Renoux, Jennifer Lunardini, Francesca Basilico, Nicola Bulgheroni, Maria Cangelosi, Angelo Cesari, Matteo Cid, Manuel Ianes, Aladar Gonzalez-Jimenez, Javier Kounoudes, Anastasis Mari, David Prisacariu, Victor Savanovic, Arso Ferrante, Simona Borghese, N. Alberto Applied Intelligent System Lab Department of Computer Science University of Milan Milano Italy Machine Perception and Intelligent Robotics Group (MAPIR) Department of System Engineering and Automation University of Malaga and Instituto de Investigación Biomédica de Málaga–IBIMA Malaga Spain Machine Perception and Interaction Lab Örebro University Örebro Sweden NearLab Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy AB.Acus SRL Milan Italy University of Manchester Manchester UK IRCCS Istituti Clinici Scientifici Maugeri University of Milan Milan Italy Junta de Extremadura Merida Spain Korian Milan Italy SignalGeneriX Limassol Cyprus EURECAT Barcelona Spain University of Oxford Oxford UK Smart Com Ljubjana Slovenia