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Identification of Functional Connectivity Features in Depression Subtypes Using a data-Driven Approach 1

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1st international Workshop on Graph learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Li, Xingjuan Burnham, Samantha Fripp, Jurgen Li, Yu Li, Xue Fazlollahi, Amir Bourgeat, Pierrick CSIRO Australian eHealth Research Centre Brisbane4029 Australia School of Information Technology and Electrical Engineering The University of Queensland Brisbane4067 Australia

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

Biomarkers are not well understood in depression, partly because there is no golden rule of what is abnormal in which patients and how neurobiological information can be used to improve diagnosis. The heterogeneity of depression suggests that diverse circuit-level abnormalities in individuals lead to various symptoms. Investigating heterogeneous depression is crucial to understand disease mechanisms and provide personalised medicine. Dynamical functional connectivity (dFC), consisting of spatial-temporal characteristics of brain activity, has been shown to be effective in characterizing the circuit-level abnormalities in depression. However, most of the current studies on dFC are based on one-step mapping while ignoring hierarchical spatial-temporal information, which may leverage the power of diagnosis in depression subtypes. In this study, we propose Brain Network Gated Recurrent Units (BrainNetGRU) to discover hierarchical resting-state dFC features for the diagnosis of depression subtypes, using data from 770 depressive adults from the UK Biobank. Particularly, we devise diffusion convolutional filters and recurrent units to effectively learn distinctive dynamic brain connectivity for depression subtypes. Experimental results show that BrainNetGRU can identify three types of depression with an accuracy of 72.05%. In addition, BrainNetGRU shows that resting-state functional connections in default mode network (DMN), cingulo-opercular network (CON) and fronto-parietal network (FPN) are important in the diagnosis of depression subtypes. © 2019, Springer Nature Switzerland AG.

关键词： Diagnosis

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Bridging the Gap between Smart Home Platforms and Machine learning using Relational Reference Attribute Grammars

Bridging the Gap between Smart Home Platforms and Machine Le...

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ACM/IEEE international conference on Model Driven engineering Languages and Systems Companion

作者： Rene Schone Johannes Mey Boqi Ren Uwe Assmann Software Technology Group Technische Universitat Dresden

ISBN: (纸本)9781728151267

Machine learning improves the development of self-adaptive systems, i.e., to react to unforeseen situations. However, state-of-the-art smart home platforms lack the ability to integrate machine learning models directly into their knowledge base. While there are machine learning approaches for smart home environments, they are usually not integrated nor represented in the middleware of smart environments. Instead, they rely on data obtained previously and use it in a form prepared for a single use case. To remedy this, our aim is to extend existing middleware platforms to integrate machine learning algorithms as first-class concepts of the knowledge base. To achieve this, we employ relational Reference Attribute Grammars to design and implement an integrated runtime model, where machine learning models can be represented and related to elements of the extended knowledge base, e.g., physical entities, location, users, and activities. Consequently, this enables using a state-of-the-art middleware to build a self-adaptive system, which integrates machine learning algorithms enabling both context-awareness and self-awareness. To showcase the feasibility of our approach, we implemented a small smart home scenario using openHAB as a middleware, in which the system learns the preferences of a user using neural networks.

关键词： Knowledge Bases Adaptive systems Middleware Smart homes Machine learning algorithms Artificial Intelligence Machine learning attribute grammars Platform Context awareness Integrate

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Development of an AR Drawing System with Point Cloud data suitable for Real-time Gripping Movement by using Kinect 22

Development of an AR Drawing System with Point Cloud Data su...

引用

22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Inatome, Hiroki Soga, Masato Wakayama Univ Grad Sch Syst Engn 930 Sakaedani Wakayama 6408510 Japan Wakayama Univ Fac Syst Engn 930 Sakaedani Wakayama 6408510 Japan

Virtual human figures have been used in the conventional human figure sketch learning support system. A learner selects one of the virtual figures for a motif. However, the learner could not intuitively manipulate the virtual human figure in real time to change the orientation of the figure for learner's favorite viewpoint. Therefore, in this research, we developed a system to change the orientation of the virtual model of human figure intuitively by using the drawing doll as a tangible interface. Specifically, it uses PCL which can acquire point cloud data by RGB-D camera of KINECT, then it acquires and tracks the three-dimensional coordinates of the real object and superimposes and displays the virtual human figure on the real drawing doll. In the verification experiment tracking accuracy was verified and the improvement of the system was discussed.

关键词： Augmented Reality Point Cloud data Sketch learning Human Body Figure

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An intelligent Framework for Issue Ticketing System based on Machine learning 22

An Intelligent Framework for Issue Ticketing System based on...

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22nd IEEE international Enterprise Distributed Object Computing conference (IEEE EDOC)

作者： Qamili, Ruanda Shabani, Shaban Schneider, Johannes HES SO Valais Wallis Inst Informat Syst Sierre Switzerland Univ Basel Dept Math & Comp Sci Basel Switzerland Univ Liechtenstein Inst Informat Syst Vaduz Liechtenstein

ISBN: (纸本)9781538641415

Due to the rapid shift of companies towards superb customer experience and satisfaction, ticketing systems have come into a prominence and represent a strategic element in business competitiveness. Different software companies have developed very effective software tools for issue tracking, nevertheless, some sub-processes and tasks within the ticketing systems are still performed manually. These manually performed tasks represent bottlenecks, especially at large organizations they result in declined productivity and increased response time. Advancements in machine learning can be used in a novel way in which they are combined with the traditional issue tracking and ticketing systems on the market, to enable optimal operational efficiency in the Customer Service and Support(CSS) Department of large-scale businesses that deal with customer reports. This paper proposes an integrated approach to customer support by treating three seemingly different bottlenecks in the ticketing system: spam detection, ticket assignment and sentiment analysis. We use primary data to implement and apply the proposed machine learning approach. The evaluation shows promising results in terms of accuracy and efficiency of our approach.

关键词： ticketing system spam detection ticket assignment sentiment analysis

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Geometric Brain Surface Network for Brain Cortical Parcellation 1

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作者： Zhang, Wen Wang, Yalin School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States

ISBN: (数字)9783030358174

ISBN: (纸本)9783030358167

A large number of surface-based analyses on brain imaging data adopt some specific brain atlases to better assess structural and functional changes in one or more brain regions. In these analyses, it is necessary to obtain an anatomically correct surface parcellation scheme in an individual brain by referring to the given atlas. Traditional ways to accomplish this goal are through a designed surface-based registration or hand-crafted surface features, although both of them are time-consuming. A recent deep learning approach depends on a regular spherical parameterization of the mesh, which is computationally prohibitive in some cases and may also demand further post-processing to refine the network output. Therefore, an accurate and fully-automatic cortical surface parcellation scheme directly working on the original brain surfaces would be highly advantageous. In this study, we propose an end-to-end deep brain cortical parcellation network, called DBPN. Through intrinsic and extrinsic graph convolution kernels, DBPN dynamically deciphers neighborhood graph topology around each vertex and encodes the deciphered knowledge into node features. Eventually, a non-linear mapping between the node features and parcellation labels is constructed. Our model is a two-stage deep network which contains a coarse parcellation network with a U-shape structure and a refinement network to fine-tune the coarse results. We evaluate our model in a large public dataset and our work achieves superior performance than state-of-the-art baseline methods in both accuracy and efficiency. © 2019, Springer Nature Switzerland AG.

关键词： Brain

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Towards smart emergency management: Trends and challenges of feature engineering 22

Towards smart emergency management: Trends and challenges of...

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22nd international Computer Science and engineering conference, ICSEC 2018

作者： Sahoh, Bukhoree Choksuriwong, Anant Department of Computer Engineering Prince of Songkla University Faculty of Engineering Thailand

ISBN: (纸本)9781538681640

Emergency events are unpredictable and undesirable situations where the authorities must respond as quick as possible. Smart Emergency Management (SEM) is a machine intelligence concept to help the authorities extract knowledge from Social Big data (SBD) automatically. This paper proposes a blueprint for machine intelligence based on machine learning for emergency management, applies it to the SEMs lifecycle framework. We present a conceptual-designed Bayesian Network as a contextual model for feature engineering which summarizes and discovers the values from the emergency-related SBD. This paper also discusses the challenges in feature engineering for the SBD-based conceptual model of SEMs. © 2018 IEEE.

关键词： Bayesian networks

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U-net fixed-point quantization for medical image segmentation 4th

U-net fixed-point quantization for medical image segmentatio...

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4th international Workshop on Large-Scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2019, the 1st international Workshop on Hardware Aware learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the 2nd international Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd international conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019

作者： AskariHemmat, MohammadHossein Honari, Sina Rouhier, Lucas Perone, Christian S. Cohen-Adad, Julien Savaria, Yvon David, Jean-Pierre Ecole Polytechnique Montreal Montreal Canada Mila-University of Montreal Montreal Canada NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montreal Montreal Canada

ISBN: (纸本)9783030336417

Model quantization is leveraged to reduce the memory consumption and the computation time of deep neural networks. This is achieved by representing weights and activations with a lower bit resolution when compared to their high precision floating point counterparts. The suitable level of quantization is directly related to the model performance. Lowering the quantization precision (e.g. 2 bits), reduces the amount of memory required to store model parameters and the amount of logic required to implement computational blocks, which contributes to reducing the power consumption of the entire system. These benefits typically come at the cost of reduced accuracy. The main challenge is to quantize a network as much as possible, while maintaining the performance accuracy. In this work, we present a quantization method for the U-Net architecture, a popular model in medical image segmentation. We then apply our quantization algorithm to three datasets: (1) the Spinal Cord Gray Matter Segmentation (GM), (2) the ISBI challenge for segmentation of neuronal structures in Electron Microscopic (EM), and (3) the public National Institute of Health (NIH) dataset for pancreas segmentation in abdominal CT scans. The reported results demonstrate that with only 4 bits for weights and 6 bits for activations, we obtain 8 fold reduction in memory requirements while loosing only 2.21%, 0.57% and 2.09% dice overlap score for EM, GM and NIH datasets respectively. Our fixed point quantization provides a flexible trade-off between accuracy and memory requirement, which is not provided by previous quantization methods for U-Net (Our code is released at https://***/hossein1387/U-Net-Fixed-Point-Quantization-for-Medical-Image-Segmentation). © Springer Nature Switzerland AG 2019.

关键词： Chemical activation

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A new incremental relational association rules mining approach 22

A new incremental relational association rules mining approa...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Miholca, Diana-Lucia Czibula, Gabriela Crivei, Liana Maria Babes Bolyai Univ Dept Comp Sci 1 M Kogalniceanu St Cluj Napoca 400084 Romania

Online data mining techniques are used to uncover relevant patterns in complex data which are dynamic by nature and thus continuously extended with real-time arriving data streams. Relational association rules (RARs), a data analysis and mining concept, extend the classical association rules so as to capture different relations between the attributes characterizing the data. This paper introduces a new Incremental Relational Association Rule Mining (IRARM) approach with the aim of progressively adapting the interesting relational association rules identified in a data set, when it is enlarged with new instances. We have experimentally evaluated IRARM on publicly available data sets. The reduction in mining time when using IRARM against mining from scratch emphasizes its efficiency in adapting the rules to real-time data extension. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： data mining Unsupervised learning Relational association rules

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Collaborative Health Care Plan through Crowdsource data using Ambient Application

Collaborative Health Care Plan through Crowdsource Data usin...

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IEEE international conference on Multi Topic

作者： Muhammad Usman Sarwar Abdul Rehman Javed National University of Computers and Emerging Sciences Islamabad Pakistan

ISBN: (数字)9781728140018

ISBN: (纸本)9781728140025

A collaborative health care plan using a multi-agent system assists adult individuals to live an independent healthy life by analyzing their routine life activities. Robust recognition of activities provides services such as health monitoring and fitness assessment. In this paper, we propose Collaborative Health Care Plan a novel system, to improve the independent living of an individual using a smartphone sensor, machine learning algorithm, multiple agents i.e. doctor, gym trainer, guardian, and intelligent ranker agent. The novelty of the devised approach is that it shares the daily life assessment of activities among care provides which in return provides a care plan or recommendation to ensure good health of the individual. We use a machine learning algorithm to recognize the adult individual's daily life physical activities. This system provides recommendations by care providers as well as suggest an optimal care plan decided by an intelligent autonomous agent using crowdsource data.

关键词： Medical services Activity recognition Monitoring Collaboration intelligent agents Accelerometers Wireless sensor networks

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Healthy Vessel Wall Detection Using U-Net in Optical Coherence Tomography 1st

Healthy Vessel Wall Detection Using U-Net in Optical Coheren...

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1st international Workshop on Machine learning and Medical engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Liu, Shengnan Shamonin, Denis P. Zahnd, Guillaume van der Steen, A.F.W. van Walsum, Theo van Soest, Gijs Department of Biomedical Engineering Erasmus MC Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands Medical Image Processing Department Leiden University Medical Center Leiden Netherlands Computer Aided Medical Procedures Technische Universität München Boltzmannstraße 3 Garching bei München85748 Germany Biomedical Imaging Group Rotterdam Department of Medical Informatics and Radiology Erasmus MC Dr. Molewaterplein 40 Rotterdam3015 GD Netherlands

ISBN: (纸本)9783030333263

Intravascular optical coherence tomography can be applied for high-resolution imaging in the coronary arteries with ischemic heart disease. The differentiation of the healthy and diseased vessel wall can be used to assess the extent and severity of coronary artery disease, and to guide therapeutic interventions. The aim of this study is to develop a recognition framework that can be potentially used for real-time intraoperative application. Structures in an image were labeled into five categories: diseased, healthy, luminal, guide-wire, and others. A U-net was implemented to directly take Cartesian images as input without any additional processing steps. A sigmoid activation and binary cross-entropy loss were applied to perform multi-labeling segmentation. Three transformations were specifically proposed in the polar domain for data augmentation. For evaluation of the proposed framework, 200 images from 20 patients were used and a triple-leave-2-out cross validation was carried out. Performance was evaluated using the average loss in the validation dataset, and the Dice scores were reported as well. Results showed that the proposed framework can perform the segmentation generally with an average performance of 0.88 ± 0.02 in Dice scores. These preliminary results suggest that the proposed framework can be potentially applied for assisting diagnosis in real-time. In the future, we intend to include more data, also take into consideration artifacts such as bad flushing, more deformed lumen, and side-branches. © 2019, Springer Nature Switzerland AG.

关键词： Optical tomography

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