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Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis

Alzheimer's and Dementia 2025年第6期21卷 e70326页

作者： Charisis, Sokratis Rashid, Tanweer Dintica, Christina Gonzales, Mitzi Liu, Hangfan Ware, Jeffrey B. Austin, Thomas R. Jensen, Paul N. Fohner, Alison E. Tanley, Jordan E. Ding, Jingzhong Luchsinger, José A. Sachs, Bonnie Nasrallah, Ilya M. Bryan, R. Nick Hayden, Kathleen M. Wolk, David Rascovsky, Katya Davatzikos, Christos Longstreth, William T. Yaffe, Kristine Seshadri, Sudha Heckbert, Susan R. Hughes, Timothy Habes, Mohamad Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC) Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases University of Texas Health Science Center San Antonio San Antonio TX United States Department of Neurology University of Texas Health Science Center at San Antonio San Antonio TX United States Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA United States Department of Neurology Cedars Sinai Medical Center Los Angeles CA United States Center for Advanced Imaging Research (CAIR) University of Maryland School of Medicine Baltimore MD United States Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Epidemiology University of Washington Seattle WA United States Cardiovascular Health Research Unit University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Department of Internal Medicine Wake Forest School of Medicine Winston-Salem NC United States Departments of Medicine and Epidemiology Columbia University Medical Center New York NY United States Department of Neurology Wake Forest University School of Medicine Winston-Salem NC United States AI2D Center for AI and Data Science for Integrated Diagnostics and Center for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA United States Department of Social Sciences and Health Policy Division of Public Health Sciences Wake Forest University School of Medicine Winston-Salem NC United States Alzheimer's Disease Research Center University of Pennsylvania Philadelphia PA United States Department of Neurology University of Pennsylvania Philadelphia PA United States Department of Neurology University of Washington Seattle WA United States

INTRODUCTION: We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance. METHODS: In 892 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery. RESULTS: A composite SVD measure was constructed as a linear combination of basal ganglia PVS, thalamus PVS, periventricular WMH, subcortical WMH, and white matter FA and TR, and exhibited associations with worse global and domain-specific cognitive performance. Additionally, SVD mediated the effect of age and cardiovascular disease risk on global cognitive function, both directly and through smaller gray matter (GM) volume. DISCUSSION: Integrating multiple individual SVD endophenotypes may more accurately reflect the neurobiology of SVD and capture its global impact on cognition. SVD mediates the effects of age and cardiovascular disease risk on cognition through both atrophy-related and non–atrophy-related pathways. Highlights: Associations between individual magnetic resonance imaging (MRI) markers of brain small vessel disease and cognitive outcomes might not fully capture the global impact of small vessel disease on cognition. We modeled small vessel disease as a latent construct, integrating multiple MRI endophenotypes in strategic brain regions. The small vessel disease construct was associated with worse global and domain-specific cognitive performance. The small vessel disease construct exhibited mediating effects in the relationships of aging and cardiovascular disease risk with cognition through pathways that both involve and are independent of brain atrophy. Integrating information from multiple relevant imaging endophenotypes could open new avenues in small vessel disease research, broadening our understanding of its risk factors

关键词： aging atrophy attention cardiovascular disease risk cerebral small vessel disease cognition cognitive performance deep learning delayed memory diffusion tensor imaging enlarged perivascular spaces executive function factor analysis fractional anisotropy Framingham risk score global cognition gray matter volume immediate memory language latent variable magnetic resonance imaging mediation microbleeds phonemic fluency processing speed semantic fluency structural equation modeling trace visuospatial functioning white matter hyperintensities

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DP03 Melanoma detection in whole-slide images using a convolutional neural network for objective prognostic biomarker generation

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British Journal of Dermatology 2024年第SUPPLEMENT_1期191卷 i108–i108页

作者： Clarke, Emily L Magee, Derek Newton-Bishop, Julia Merchant, William Hall, Marlous Insall, Robert Maher, Nigel G Scolyer, Richard A Farnworth, Grace Ali, Anisah Saldanha, Gerald Bamford, Mark Sitcova, Eva Kujal, Petr O’Shea, Sally Treanor, Darren Department of Histopathology Leeds Teaching Hospitals NHS Trust Leeds UK Division of Pathology and Data Analytics University of Leeds Leeds UK School of Computing University of Leeds Leeds UK Leeds Institute for Medical Research University of Leeds Leeds UK Department of Clinical and Population Sciences Leeds Institute of Cardiovascular and Metabolic Medicine University of Leeds Leeds UK Department of Cell and Developmental Biology University College London London UK Melanoma Institute Australia The University of Sydney Sydney Australia Faculty of Medicine and Health The University of Sydney Sydney Australia Tissue Pathology and Diagnostic Oncology Royal Prince Alfred Hospital and NSW Health Pathology Sydney Australia Charles Perkins Centre The University of Sydney Sydney NSW Australia Leicester Cancer Research Centre University of Leicester Leicester UK Department of Histopathology University Hospitals of Leicester NHS Trust Leicester UK Department of Pathology Third Faculty of Medicine Charles University and Teaching Hospital Královské Vinohrady Prague Czechia Dermatology Department South Infirmary Victoria University Hospital Cork Ireland School of Medicine University College Cork Ireland Department of Clinical Pathology and Department of Clinical and Experimental Medicine Linköping University Linköping Sweden Center for Medical Image Science and Visualization (CMIV) Linköping University Linköping Sweden

The current melanoma staging system is predictive of 74% of the variance in survival, with prognostic biomarkers subject to high levels of interobserver and intraobserver variation. Melanoma morphology appears to be of greater significance than in other solid tumours, with Breslow thickness remaining the strongest prognostic indicator. The application of convolutional neural networks (CNNs) to whole-slide images (WSIs) may reveal new insights into tumour morphology and therefore patient prognosis. This work outlines the development and evaluation of a CNN for invasive cutaneous melanoma detection in WSIs, to enable the creation of objective prognostic biomarkers based on the tissue morphology. In total, 1157 WSIs containing cutaneous melanoma from five sources have been used in the initial development and evaluation of a custom-designed two-class tumour segmentation network with a fully convolutional architecture. The CNN detected and located invasive melanoma tissue of no specific type with an average per-pixel sensitivity and specificity of 97.6% and 99.9%, respectively across, the five test sets (three external). There were no statistical differences between tumour dimensions generated by the CNN compared with manual annotation. Similarly, there were no statistically significant differences between CNN-generated tumour dimensions across three scanning platforms. Furthermore, we have identified that this CNN can be used to calculate the ‘digital Breslow thickness’, which is a strong independent prognostic predictor of overall survival and melanoma-specific survival, across three test sets with follow-up data (hazard ratio 1.26, 95% confidence interval 1.19–1.34,P< 0.001). We have also shown that the ‘nodularity index’ determined by the tumour shape, independently of size, is predictive of survival; the rounder the tumour the worse the outcome (hazard ratio 0.71, 95% confidence interval 0.60–0.83,P< 0.001). We have developed and performed initial evaluation of a CN

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A progressively-trained scale-invariant and boundary-aware deep neural network for the automatic 3D segmentation of lung lesions

arXiv

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arXiv 2018年

作者： Zhou, Bo Crawford, Randolph Dogdas, Belma Goldmacher, Gregory Chen, Antong School of Computer Science Carnegie Mellon University PittsburghPA United States Image Data Analytics Merck & Co. Inc. West PointPA United States Translational Biomarkers Merck & Co. Inc. West PointPA United States

Volumetric segmentation of lesions on CT scans is important for many types of analysis, including lesion growth kinetic modeling in clinical trials and machine learning of radiomic features. Manual segmentation is laborious, and impractical for large-scale use. For routine clinical use, and in clinical trials that apply the Response Evaluation Criteria In Solid Tumors (RECIST), clinicians typically outline the boundaries of a lesion on a single slice to extract diameter measurements. In this work, we have collected a large-scale database, named LesionVis, with pixel-wise manual 2D lesion delineations on the RECIST-slices. To extend the 2D segmentations to 3D, we propose a volumetric progressive lesion segmentation (PLS) algorithm to automatically segment the 3D lesion volume from 2D delineations using a scale-invariant and boundary-aware deep convolutional network (SIBA-Net). The SIBA-Net copes with the size transition of a lesion when the PLS progresses from the RECIST-slice to the edge-slices, as well as when performing longitudinal assessment of lesions whose size change over multiple time points. The proposed PLS-SiBA-Net (P-SiBA) approach is assessed on the lung lesion cases from LesionVis. Our experimental results demonstrate that the P-SiBA approach achieves mean Dice similarity coefficients (DSC) of 0.81, which significantly improves 3D segmentation accuracy compared with the approaches proposed previously (highest mean DSC at 0.78 on LesionVis). In summary, by leveraging the limited 2D delineations on the RECIST-slices, P-SiBA is an effective semi-supervised approach to produce accurate lesion segmentations in 3D. Copyright © 2018, The Authors. All rights reserved.

关键词： Computerized tomography

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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SaltiNet: Scan-Path Prediction on 360 Degree images Using Saliency Volumes

SaltiNet: Scan-Path Prediction on 360 Degree Images Using Sa...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Marc Assens Xavier Giro-i-Nieto Kevin McGuinness Noel E. O’Connor Image Processing Group Universitat Politecnica de Catalunya (UPC) Catalonia Barcelona Spain Insight Center for Data Analytics Dublin City University Dublin Ireland

We introduce SaltiNet, a deep neural network for scan-path prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scan-paths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at https://***/massens/saliency-360salient-2017.

关键词： Training Computational modeling Predictive models Visualization Observers Biological system modeling

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Nonconvulsive epileptic seizures detection using multiway data analysis 25

Nonconvulsive epileptic seizures detection using multiway da...

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25th European Signal Processing Conference, EUSIPCO 2017

作者： Aldana, Yissel Rodríguez Hunyadi, Borbála Reyes, Enrique J. Maranón Rodríguez, Valia Rodríguez Van Huffel, Sabine Universidad de Oriente Center of Neuroscience and Signals and Image Processing Santiago de Cuba Cuba KU Leuven Stadius Center for Dynamical Systems Signal Processing and Data Analytics IMEC Leuven BE Belgium Cuban Neuroscience Center ICU-Ameijeiras Clinic Surgical Hospital La Habana Cuba Aston University United Kingdom

ISBN: (纸本)9780992862671

Nonconvulsive status epilepticus (NCSE) is observed when the patient undergoes a persistent electroencephalographic epileptic episode without physical symptoms. This condition is commonly found in critically ill patients from intensive care units and constitutes a medical emergency. This paper proposes a method to detect nonconvulsive epileptic seizures (NCES). To perform the NCES detection the electroencephalogram (EEG) is represented as a third order tensor with axes frequency × time × channels using Wavelet or Hilbert-Huang transform. The signatures obtained from the tensor decomposition are used to train five classifiers to separate between the normal and seizure EEG. Classification is performed in two ways: (1) with each signature of the different modes separately, (2) with all signatures assembled. The algorithm is tested on a database containing 139 nonconvulsive seizures. From all performed analysis, Hilbert-Huang Tensors Space and assembled signatures demonstrate to be the best features to classify between seizure and non-seizure EEG. © EURASIP 2017.

关键词： Electroencephalography

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Shallow and deep convolutional networks for saliency prediction

Shallow and deep convolutional networks for saliency predict...

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2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

作者： Pan, Junting Sayrol, Elisa Giro-I-Nieto, Xavier McGuinness, Kevin O'Connor, Noel E. Image Processing Group Universitat Politecnica de Catalunya Barcelona Catalonia Spain Insight Center for Data Analytics Dublin City University Dublin Ireland

ISBN: (纸本)9781467388511

The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors' knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.

关键词： Forecasting

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Nonconvulsive Epileptic Seizures Detection Using Multiway data Analysis

Nonconvulsive Epileptic Seizures Detection Using Multiway Da...

引用

European Signal Processing Conference

作者： Yissel Rodriguez Aldana Borbala Hunyadi Enrique J. Maranon Reyes Valia Rodriguez Rodriguez Sabine Van Huffel Center of Neuroscience and Signals and Image Processing Universidad de Oriente Cuba Signal Processing and Data Analytics KU Leuven Leuven Belgium Center of Neuroscience and Signals and Image Processing Universidad de Oriente Santiago de Cuba Santiago de Cuba CU Cuban Neuroscience Center ICU-Ameijeiras Clinic Surgical Hospital Cuba Aston University United Kingdom Stadius Center for Dynamical Systems KU Leuven Leuven BE Belgium

Nonconvulsive status epilepticus (NCSE) is observed when the patient undergoes a persistent electroencephalographic epileptic episode without physical symptoms. This condition is commonly found in critically ill patients from intensive care units and constitutes a medical emergency. This paper proposes a method to detect nonconvulsive epileptic seizures (NCES). To perform the NCES detection the electroencephalogram (EEG) is represented as a third order tensor with axes frequency x time x channels using Wavelet or Hilbert-Huang transform. The signatures obtained from the tensor decomposition are used to train five classifiers to separate between the normal and seizure EEG. Classification is performed in two ways: (1) with each signature of the different modes separately, (2) with all signatures assembled. The algorithm is tested on a database containing 139 nonconvulsive seizures. From all performed analysis, Hilbert-Huang Tensors Space and assembled signatures demonstrate to be the best features to classify between seizure and non-seizure EEG.

关键词： Tensile stress Electroencephalography Wavelet transforms Time-frequency analysis Europe Signal processing Support vector machines

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SPATIAL WINDOWING FOR CORRELATION FILTER BASED VISUAL TRACKING

SPATIAL WINDOWING FOR CORRELATION FILTER BASED VISUAL TRACKI...

引用

IEEE International Conference on image Processing

作者： Erhan Gundogdu A. Aydin Alatan Intelligent Data Analytics Research Program Dept. Aselsan Research Center Center for Image Analysis (OGAM) METU Ankara

ISBN: (纸本)9781467399623

Correlation filters have been extensively studied to address online visual object tracking task, while achieving favourable performance against the-state-of-the-art methods in various benchmark datasets. Nevertheless, undesired conditions, i.e. partial occlusions or abrupt deformations of the object appearance, severely degrade the performance of correlation filter based tracking methods. To this end, we propose a method for estimating a spatial window for the object observation such that the correlation output of the correlation filter and the windowed observation (i.e. element-wise multiplication of the window and the observation) is improved, especially in these adverse conditions. This approach leads to a performance uplift in the tracking result compared to the classical windowing operation. Moreover, the estimated spatial window of the object patch indicates the object regions that are useful for correlation. We observe a considerable amount of performance increase in the benchmark video sequences by using the proposed visual tracking method.

关键词： Correlation Visual tracking Windowing visual tracking multiprogramming Filters windows window windows

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