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18th IEEE International conference on Cognitive Informatics and Cognitive computing (ICCI*CC)

作者： Mustansar, Zartasha Rathore, Maria Shaukat, Arslan Nadeem, Faizan Farooq, Nabisha Sherbaz, Salma Natl Univ Sci & Technol Res Ctr Modeling & Simulat Islamabad Pakistan Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad Pakistan INMOL Hop Lahore Pakistan

ISBN: (纸本)9781728114194

Developing geometries of the real objects using computer aided engineering methods has been a common practice now. However, due to the evolution of advancement and launch of digital age, there is a recent interest to develop refined, smooth and entirely significant geometrical details to account for accuracy in predictions. Whether it is a scientific computation or reverse engineering, simulation or geometrical reconstruction;data sets with delicate geometric details are created quite often for various purposes. The usefulness of such geometric details rests on the ability to process them efficiently i.e. from digital models to numerical models and eventually for high-end visualization data analysis. In the field of biomedical engineering, geometry plays a very important role in model prediction. This study therefore considers the significance of geometry in the human head model to calculate critical pressure in the brain named "Intra-Cranial Pressure". Elevated intracranial pressure (ICP) is one of the common consequences of traumatic conditions and has a profound influence on outcome. There are well established methods for the measurement, continuous monitoring and treatment of raised ICP. However, there is a need to build computer models for the same for validation and prediction. We made use of a tumour brain, in this study to see how geometry varies the values acquired for ICP in the brain. One of the major benefits of this study will be non-invasive computation of pressure inside the brain in a safe frequency range. It is well established that the relation between volume and pressure is non-linear. Additionally, skull is usually, considered as an enclosed and in-elastic container like a sac. The positioning of layers within this sac generates a constant pressure which is normal according to the body homeostasis. An increase in the volume of any of the intra cranial contents (Sac contents) is naturally offset by a decrease in pressure in one or the other conten

关键词： Finite Element method Intra Cranial Pressure Mesh Density Human Brain

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Algorithms of Experimental Medical Data Analysis

Algorithms of Experimental Medical Data Analysis

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Computer Science and Software Engineering (CSASE), conference on

作者： Yousif A. Hamad Mohammed N. Qasim Ayvar A. Rashid Mohammed E. Seno Siberian Federal University Krasnoyarsk Russian Federation Department of software engendering Imam Jaafar AL-Sadiq University Kirkuk Iraq Department of Computer Science Al-Maarif University College Anbar Iraq

ISBN: (数字)9781728152493

ISBN: (纸本)9781728152509

The paper is devoted to the development of a computational technique for assessing the performance of tissue regeneration in an experiment using mesh nickel-titanium implants with shape memory. Observational data obtained from electron microscopy and classical histological examination are processed and analyzed by the use of proprietary algorithms and their modifications. This can significantly facilitate the procedure of data analysis and increase the accuracy of the estimates by 5-10%. As a computational method for examining the dynamics of the studied process and determining the internal geometrical characteristics of empirical images of objects concerned, the suggested technique includes algorithms of shearlet transform, wavelet transform and construction of elastic maps for efficient visualization of spatial data. The significant part of the recommended method is the computing means of visual data preprocessing for increasing the brightness and contrast of the examined images based on the Retinex technology. This part has a significant impact on the quality of applying the tools of the computer-based evaluation presented in this work.

关键词： biomedical imaging Brightness Implants Image color analysis Wavelet transforms Software algorithms

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Explainable Artificial Intelligence Applications in NLP, biomedical, and Malware Classification: A Literature Review

Explainable Artificial Intelligence Applications in NLP, Bio...

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computing conference, 2019

作者： Mathews, Sherin Mary McAfee LLC Santa ClaraCA95054 United States

ISBN: (纸本)9783030228675

Deep learning algorithms have achieved high performance accuracy in complex domains such as image classification, face recognition sentiment analysis, text classification, and speech understanding. Due to the nested non-linear structure of deep learning algorithms, these highly successful models are usually applied in a black-box manner, i.e., no information is provided about what exactly causes them to arrive at their predictions. The effectiveness of these systems is thus limited by the machine’s current inability to explain its decisions and actions to human users. Such a lack of transparency can be a major drawback. For instance, in medical applications the development of methods for visualizing, explaining, and interpreting deep learning models has recently attracted increasing attention. Explainable Machine Learning (XAI) or Interpretable Machine Learning (IML) programs aim to create a suite of machine learning techniques that produce more explainable models while maintaining the high level of accuracy. It also enables human users to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners. Our work summarizes recent developments in this field, and we present three classification tasks wherein we make use of LIME (Local Interpretable Model-Agnostic Explanations) to explain predictions of deep learning models. LIME attempts to make these complex models at least partly understandable by evaluating using three classification tasks. Our first evaluation is performed on natural language processing application for tweet data classification and our second evaluation is on biomedical signal classification cancer detection. Our third evaluation is on Windows PC malware classification in a cybersecurity domain. In all three case studies, the interpreted results are presented in terms of visualization. © 2019, Springer Nature Switzerland AG.

关键词： Sensitivity analysis

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Soft Real Time Data Driven IoT for Knee Rehabilitation

Soft Real Time Data Driven IoT for Knee Rehabilitation

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IEEE conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)

作者： S.M.N. Arosha Senanayake Putri Wulandari Mathematical and Computing Sciences Universiti Brunei Darussalam Brunei Darussalam Electrical Engineering Universitas Al Azhar Indonesia Jakarta Indonesia

ISBN: (数字)9781728194370

ISBN: (纸本)9781728194387

This article presents soft real time data driven Internet of Things (IoT) for knee rehabilitation using cyber physical sensory information system interfaced with cloud storage. Custom made wearable wireless motion capture suit interfaced to smart watch as the IoT are built for biofeedback visualization. Mullti-sensor integration and data fusion mechanisms are employed to obtain input vectors of knowledge base and the output vector is based on patient classification defined using multivariate statistics by the healthcare professionals. Case based reasoning is applied for the established reference standard in order to produce patient centric actual knee rehabilitation status and classification using semi supervised deep learning method. Wearable IoT is automatically updated the actual knee rehabilitation status and classification of a patient using relevant cyber physical sensory information retrieved from the cloud storage connected vis AWS cloud. Hence, a soft real time data drive IoT for knee rehabilitation system is successfully tested and validated using semi supervised deep learning cyber physical sensory information database subject to statistically quantified parameters by health professionals based on principle component analysis and patient centric parameters based on independent component analysis. The data driven IoT built has been validated in rehabilitation clinics by relevant physiotherapists and patients with the average age of ±36.8.

关键词： Deep learning Cloud computing Medical services Real-time systems Internet of Things biomedical monitoring Information systems

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Neuron shapley: discovering the responsible neurons 20

Neuron shapley: discovering the responsible neurons

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Proceedings of the 34th International conference on Neural Information Processing Systems

作者： Amirata Ghorbani James Zou Department of Electrical Engineering Stanford University Stanford CA Department of Biomedical Data Science Stanford University Stanford CA

ISBN: (纸本)9781713829546

We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network. By accounting for interactions across neurons, Neuron Shapley is more effective in identifying important filters compared to common approaches based on activation patterns. Interestingly, removing just 30 filters with the highest Shapley scores effectively destroys the prediction accuracy of Inception-v3 on ImageNet. visualization of these few critical filters provides insights into how the network functions. Neuron Shapley is a flexible framework and can be applied to identify responsible neurons in many tasks. We illustrate additional applications of identifying filters that are responsible for biased prediction in facial recognition and filters that are vulnerable to adversarial attacks. Removing these filters is a quick way to repair models. computing exact Shapley values is computationally infeasible and therefore sampling-based approximations are used in practice. We introduce a new multi-armed bandit algorithm that is able to efficiently detect neurons with the largest Shapley value orders of magnitude faster than existing Shapley value approximation methods.

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A Water Quality Research Platform for the Near-real-time Buoy Sensor Data

A Water Quality Research Platform for the Near-real-time Buo...

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IEEE International conference on Information Reuse and Integration (IRI)

作者： Maria Presa-Reyes Biayna Bogosian Bradley Schonhoff Christopher Jerauld Christian Moreyra Piero Gardinali Shu-Ching Chen School of Computing and Information Sciences Florida International University Miami Florida Department of Architecture Florida International University Miami Florida Institute of Environment Florida International University Miami Florida Department of Chemistry and Biochemistry Florida International University Miami Florida

ISBN: (数字)9781728110547

ISBN: (纸本)9781728110554

Maintaining environmental sustainability relies on continuously monitoring environmental conditions. Water is an environmental component essential to the survival of all living organisms; hence, to prevent contamination and ensure proper water treatment, persistent observations and measurements of water quality are crucial. Traditionally, the procedure for testing the quality of water involved traveling to designated testing sites, manually collecting surface samples, transporting said samples to a laboratory for analysis, analyzing chemicals and microbial contaminants, and publishing the findings with the community. The technological advances in wireless sensor networks bring forth the opportunity for remote measurement and monitoring of water samples. Not only is the presence of the scientist no longer mandatory on the testing site, but the data can also be automatically collected, visualized, monitored, and shared through sensor recordings. These transitions exhibit a much fine-grained level of spatio-temporal information collection and allow for more comprehensive and long-term studies. Three research buoys, designed to be deployed in both shallow freshwater ecosystems and near-shore marine environments, were launched in different locations of South Florida to tackle complex challenges of environmental contamination. The research presented here designs and deploys a water quality monitoring platform for allowing the scientists to analyze better the near-real-time data collected by the buoys and generate insights. We further demonstrate two engaging near-real-time visualization methods developed to disseminate data trends and findings to a wide range of audiences from diverse backgrounds.

关键词： Monitoring Water pollution Data visualization Ecosystems Pollution measurement Testing biomedical monitoring

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The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv

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arXiv 2021年

作者： Baheti, Bhakti Chakrabarty, Satrajit Akbari, Hamed Bilello, Michel Wiestler, Benedikt Schwarting, Julian Calabrese, Evan Rudie, Jeffrey Abidi, Syed Mousa, Mina Villanueva-Meyer, Javier Fields, Brandon K.K. Kofler, Florian Shinohara, Russell Takeshi Iglesias, Juan Eugenio Mok, Tony C.W. Chung, Albert C.S. Wodzinski, Marek Jurgas, Artur Marini, Niccolò Atzori, Manfredo Müller, Henning Grosbröhmer, Christoph Siebert, Hanna Hansen, Lasse Heinrich, Mattias P. Canalini, Luca Klein, Jan Gerken, Annika Heldmann, Stefan Hering, Alessa Hahn, Horst K. Meng, Mingyuan Bi, Lei Feng, Dagan Kim, Jinman Zeineldinal, Ramy A. Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Abderezaei, Javid Pionteck, Aymeric Chopra, Agamdeep Kurt, Mehmet Yan, Kewei Yan, Yonghong Tang, Zhe Ma, Jianqiang Nasser, Sahar Almahfouz Kurian, Nikhil Cherian Meena, Mohit Shamsi, Saqib Sethi, Amit Tustison, Nicholas J. Avants, Brian B. Cook, Philip Gee, James C. Tian, Lin Greer, Hastings Niethammer, Marc Hoopesar, Andrew Hoffmannas, Malte Dalcaar, Adrian V. Christodoulidis, Stergios Estiene, Théo Vakalopoulou, Maria Paragios, Nikos Marcus, Daniel S. Davatzikos, Christos Sotiras, Aristeidis Menze, Bjoern Bakas, Spyridon Waldmannstetter, Diana Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Department of Electrical and Systems Engineering Washington University in St. Louis St. LouisMO United States GE HealthCare San RamonCA United States University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Mallinckrodt Institute of Radiology Washington University School of Medicine St. LouisMO United States Institute for Informatics Data Science & Biostatistics Washington University School of Medicine St. LouisMO United States Department of Bioengineering Santa Clara University Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Munich Germany Department of Nuclear Medicine University Hospital Regensburg Regensburg Germany University Hospital Regensburg Regensburg Germany Department of Radiology & Biomedical Imaging University of California San FranciscoCA United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Radiology University of California San DiegoCA United States Helmholtz AI Helmholtz Munich Neuherberg Germany Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia United States Massachusetts General Hospital Harvard Medical School United States Centre for Medical Imaging Computing University College London United Kingdom The Hong Kong University of Science and Technology Hong Kong University of Applied Sciences West

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on biomedical Imaging (ISBI) 2022 and the International conference on Medical Image computing and Computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency

关键词： Magnetic resonance imaging

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Interactive Tool Using Augmented Reality (AR) for Learning Knee and Foot Anatomy Based on CT Images 3D Reconstruction

Interactive Tool Using Augmented Reality (AR) for Learning K...

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Computational Intelligence (ICCI),International conference on

作者： Liew Set Lee Sayed Ahmad Zikri Sayed Aluwee Goh Chuan Meng Pradeep Palanisamy Ramani Subramaniam Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Universiti Kuala Lumpur Kampus Cawangan Royal College of Medicine Perak (RCMP) Ipoh Malaysia Radiology Discipline Sub Unit Universiti Kuala Lumpur Kampus Cawangan Royal College of Medicine Perak (RCMP) Ipoh Malaysia

ISBN: (数字)9781728184081

ISBN: (纸本)9781728184104

Anatomy is the branch of biological science in medical education that focuses on structured parts of living things, especially the human body. Traditional teaching methods and learning materials of human body anatomy are usually available in the form of textbooks with pictures and images or artificial anatomy mannequins. There are still not enough to help the students in understanding it with actual and accurate knowledge about our human body anatomy. It is because students are challenging in learning the human anatomy body part by through imagining it's real and lack of interaction and hard to understand with those 2D images model on the textbooks. Although there are artificial anatomy mannequins available for learning, it is limited in number and access. Technological developments, especially applications based on 3D, are expected to help the learning process of this science subject. In this study, we proposed to develop an augmented reality (AR) mobile application for learning human anatomy knee and foot through medical 3-dimensional (3D) reconstruction based on medical images. By using this application, students expected can easily understand human anatomy using 3D image visualisation on the mobile computing platform.

关键词： Knee visualization Image segmentation Three-dimensional displays Human anatomy Learning (artificial intelligence) biomedical imaging

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Towards Quantifying Neurovascular Resilience 1st

Towards Quantifying Neurovascular Resilience

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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： Moriconi, Stefano Rehwald, Rafael Zuluaga, Maria A. Jäger, H. Rolf Nachev, Parashkev Ourselin, Sébastien Cardoso, M. Jorge School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Institute of Neurology University College London London United Kingdom Universidad Nacional de Colombia Bogotá Colombia

ISBN: (纸本)9783030333263

Whilst grading neurovascular abnormalities is critical for prompt surgical repair, no statistical markers are currently available for predicting the risk of adverse events, such as stroke, and the overall resilience of a network to vascular complications. The lack of compact, fast, and scalable simulations with network perturbations impedes the analysis of the vascular resilience to life-threatening conditions, surgical interventions and long-term follow-up. We introduce a graph-based approach for efficient simulations, which statistically estimates biomarkers from a series of perturbations on the patient-specific vascular network. Analog-equivalent circuits are derived from clinical angiographies. Vascular graphs embed mechanical attributes modelling the impedance of a tubular structure with stenosis, tortuosity and complete occlusions. We evaluate pressure and flow distributions, simulating healthy topologies and abnormal variants with perturbations in key pathological scenarios. These describe the intrinsic network resilience to pathology, and delineate the underlying cerebrovascular autoregulation mechanisms. Lastly, a putative graph sampling strategy is devised on the same formulation, to support the topological inference of uncertain neurovascular graphs. © 2019, Springer Nature Switzerland AG.

关键词： Grading

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Topic modeling for systematic review of visual analytics in incomplete longitudinal behavioral trial data

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Smart Health 2020年 18卷 100142页

作者： Rumbut, Joshua Fang, Hua Wang, Honggong Department of Computer and Information Science University of Massachusetts Dartmouth North Dartmouth 02747 MA United States Department of Electrical and Computer Engineering University of Massachusetts Dartmouth North Dartmouth 02747 MA United States Department of Population and Quantitative Health Sciences University of Massachusetts Medical School Worcester 01655 MA United States

Longitudinal observational and randomized controlled trials (RCT) are widely applied in biomedical behavioral studies and increasingly implemented in smart health systems. These trials frequently produce data that are high-dimensional, correlated, and contain missing values, posing significant analytic challenges. Notably, visual analytics are underdeveloped in this area. In this paper, we developed a longitudinal topic model to implement the systematic review of visual analytic methods presented at the IEEE VIS conference over its 28 year history, in comparison with MIFuzzy, an integrated and comprehensive soft computing tool for behavioral trajectory pattern recognition, validation, and visualization of incomplete longitudinal data. The findings of our longitudinal topic modeling highlight the trend patterns of visual analytics development in longitudinal behavioral trials and underscore the gigantic gap of existing robust visual analytic methods and actual working algorithms for longitudinal behavioral trial data. Future research areas for visual analytics in behavioral trial studies and smart health systems are discussed. © 2020

关键词： MIFuzzy Missing data Systematic review Topic modeling Visual analytics visualization

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