检索结果-内蒙古大学图书馆

Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis

Information Fusion 2025年 124卷

作者： Misbah Ahmad Imran Ahmed Abdellah Chehri Gwangill Jeon Centre for Machine Vision Bristol Robotics Laboratory University of West of England Frenchy Campus Bristol BS16 1QY Bristol UK Department of Animal and Agriculture Hartpury University Hartpury House Gloucester GL19 3BE Gloucestershire UK School of Computing and Information Science Anglia Ruskin University East Road Cambridge CB1 1PT Cambridgeshire UK Department of Mathematics and Computer Science Royal Military College of Canada (RMC) P.O. Box 17000 Station Forces Kingston K7K 7B4 Ontario Canada Department of Embedded Systems Engineering Incheon National University 119 Academy-ro Incheon 22012 Incheon Republic of Korea

In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance. The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata. The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification. Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification. Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quantitative susceptibility atlas construction in montreal neurological institute space: Towards histological-consistent Iron-rich deep brain nucleus sub-region identification

Research Square

引用

Research Square 2021年

作者： He, Chenyu Guan, Xiaojun Zhang, Weimin Li, Jun Liu, Chunlei Wei, Hongjiang Xu, Xiaojun Zhang, Yuyao School of Information Science and Technology ShanghaiTech University 393 Huaxia Road Shanghai201210 China Department of Radiology Second Affiliated Hospital Zhejiang University School of Medicine 88 Jiefang Road Hangzhou Zhejiang 310009 China Electrical Engineering and Computer Science University of California at Berkeley BerkeleyCA94720 United States School of Biomedical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200030 China Institute of Medical Robotics Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200030 China Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University 393 Huaxia Road Shanghai201210 China

Iron-rich deep brain nuclei (DBN) of the human brain are involved in various motoric, emotional and cognitive brain functions. The abnormal iron alterations in the DBN are closely associated with multiple neurological and psychiatric diseases. Quantitative susceptibility mapping (QSM) provides the spatial distribution of tissue magnetic susceptibility in the human brain. Compared to traditional structural imaging, QSM has superiority for imaging the iron-rich DBN owing to the susceptibility difference existing between brain tissues. In this study, we construct a Montreal Neurological Institute (MNI) space unbiased QSM human brain atlas via group-wise registration from 100 healthy subjects aged 19-29 years. The atlas construction process is guided by hybrid images that fused from multi-modal Magnetic Resonance Images (MRI), thus named as Multi-modal-fused magnetic Susceptibility (MuSus-100) atlas. The high-quality susceptibility atlas provides extraordinary image contrast between iron-rich DBN with their surroundings. Parcellation maps of DBN and their sub-regions that are highly related to neurological and psychiatric pathology are then manually labeled based on the atlas set with the assistance of an image border-enhancement process. Especially, the bilateral thalamus is delineated into 64 detailed sub-regions referring to the Schaltenbrand and Wahren stereotactic atlas. To our best knowledge, the histological-consistent thalamic nucleus parcellation map is well defined for the first time in MNI space. Comparing with existing atlases emphasized on DBN parcellation, the newly proposed atlas outperforms on atlas-guided individual brain image DBN segmentation accuracy and robustness. Moreover, we apply the proposed DBN parcellation map to conduct detailed identification of the pathology-related iron content alterations in subcortical nuclei for Parkinson Disease (PD) patients. We envision that the MuSus-100 atlas could play a crucial role in improving the accuracy of

关键词： Brain

来源：评论

学校读者我要写书评

暂无评论

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

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning

arXiv

引用

arXiv 2020年

作者： Li, Wenhao Jin, Bo Wang, Xiangfeng Yan, Junchi Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201804 China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai200062 China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a flexible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multiagent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Gradient methods

来源：评论

学校读者我要写书评

暂无评论

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

引用

arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

PitVis-2023 Challenge: Workflow Recognition in videos of Endoscopic Pituitary Surgery

arXiv

引用

arXiv 2024年

作者： Das, Adrito Khan, Danyal Z. Psychogyios, Dimitrios Zhang, Yitong Hanrahan, John G. Vasconcelos, Francisco Pang, You Chen, Zhen Wu, Jinlin Zou, Xiaoyang Zheng, Guoyan Qayyum, Abdul Mazher, Moona Razzak, Imran Li, Tianbin Ye, Jin He, Junjun Plotka, Szymon Kaleta, Joanna Yamlahi, Amine Jund, Antoine Godau, Patrick Kondo, Satoshi Kasai, Satoshi Hirasawa, Kousuke Rivoir, Dominik Pérez, Alejandra Rodriguez, Santiago Arbeláez, Pablo Stoyanov, Danail Marcus, Hani J. Bano, Sophia Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Department of Neurosurgery National Hospital for Neurology and Neurosurgery London United Kingdom HKISI CAS China Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China National Heart and Lung Institute Faculty of Medicine Imperial College London United Kingdom Centre for Medical Image Computing University College London London United Kingdom University of New South Wales Sydney Australia Shanghai AI Lab Shanghai China Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center University of Amsterdam Amsterdam Netherlands Sano Center for Computational Medicine Krakow Poland Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg a partnership between DKFZ University Hospital Heidelberg Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Muroran Institute of Technology Hokkaido Japan Niigata University of Health and Welfare Niigata Japan Konica Minolta Inc. Osaka Japan National Center for Tumor Diseases Dresden Germany Centre for Tactile Internet TUD Dresden Germany Universidad de los Andes Bogota Colombia DKFZ UKDD TUD Germany

The field of computer vision applied to videos of minimally invasive surgery is ever-growing. Workflow recognition pertains to the automated recognition of various aspects of a surgery: including which surgical steps are performed;and which surgical instruments are used. This information can later be used to assist clinicians when learning the surgery;during live surgery;and when writing operation notes. The Pituitary vision (PitVis) 2023 Challenge tasks the community to step and instrument recognition in videos of endoscopic pituitary surgery. This is a unique task when compared to other minimally invasive surgeries due to the smaller working space, which limits and distorts vision;and higher frequency of instrument and step switching, which requires more precise model predictions. Participants were provided with 25-videos, with results presented at the MICCAI-2023 conference as part of the Endoscopic vision 2023 Challenge in Vancouver, Canada, on 08-Oct-2023. There were 18-submissions from 9-teams across 6-countries, using a variety of deep learning models. A commonality between the top performing models was incorporating spatio-temporal and multi-task methods, with greater than 50% and 10% macro-F1-score improvement over purely spacial single-task models in step and instrument recognition respectively. The PitVis-2023 Challenge therefore demonstrates state-of-the-art computer vision models in minimally invasive surgery are transferable to a new dataset, with surgery specific techniques used to enhance performance, progressing the field further. Benchmark results are provided in the paper, and the dataset is publicly available at: https://***/10.5522/04/26531686. Copyright © 2024, The Authors. All rights reserved.

关键词： Endoscopy

来源：评论

学校读者我要写书评

暂无评论

Metrics Reloaded: Recommendations for image analysis validation

arXiv

引用

arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

引用

Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CrowdWatcher: an open-source platform to catch the eye of the crowd

引用

Quality and User Experience 2019年第1期4卷 1-17页

作者： Lebreton, Pierre Hupont, Isabelle Hirth, Matthias Mäki, Toni Skodras, Evangelos Schubert, Anton Raake, Alexander Group of Networked Sensing and Control (NeSC) Zhejiang University Hangzhou China Institute of Intelligent Systems and Robotics Sorbonne University Paris France User-Centric Analysis of Multimedia Data Group Technische Universität Ilmenau Ilmenau Germany Department of Computer Science Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Patras Patras Greece Audio Visual Technology Group Technische Universität Ilmenau Ilmenau Germany

This paper presents the open-source eye tracking platform CrowdWatcher. It enables researchers to measure gaze location and user engagement in a crowdsourcing context through traditional RGB webcams. The proposed platform particularly advances the field of Quality of Experience (QoE) research, as it allows the experimenter to collect remotely and with very limited effort novel information from crowds of participants, such as their commitment towards a task, attention and decision-making processes. Two different experiments are described that were conducted to demonstrate the platform’s potential. The first experiment addresses the measurement of participants’ behavior while performing a movie selection task. Results show that the platform provides complementary information to traditional self-reported data by taking gaze analysis into account. This is of particular relevance, since in a crowdsourcing context decision processes and attention are difficult to assess, and there is often limited control over the engagement of the test user with the task. A second experiment is conducted in the scenario of a multimedia QoE test. Prediction accuracy is compared to a professional infrared eye tracker. While CrowdWatcher performs less well than the professional eye tracker, it is still able to collect valuable gaze information in the far more challenging environment of crowdsourcing. As an outlook to further application domains, the usage of the platform to measure user engagement allows participants who do not pay attention to the task to be identified.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Gaussian curvature filter on 3d mesh

arXiv

引用

arXiv 2020年

作者： Tang, Wenming Gong, Yuanhao Liu, Kanglin Liu, Jun Pan, Wei Liu, Bozhi Qiu, Guoping College of Information Engineering Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Mechanical & Automotive Engineering South China University of Technology Department of Research and Development OPT Machine Vision Tech Co. Ltd Jinsheng Road Changan Dongguan Guangdong523860 China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom

Minimizing Gaussian curvature of meshes is fundamentally important for obtaining smooth and developable surfaces. However, there is a lack of computationally efficient and robust Gaussian curvature optimization method. In this paper, we present a simple yet effective method that can efficiently reduce Gaussian curvature for 3D meshes. We first present the mathematical foundation of our method, which states that for any point on a developable surface there must be another point that lives on its tangent plane. Then, based on this theoretical insight, we introduce a simple and robust implicit Gaussian curvature optimization method named Gaussian Curvature Filter (GCF). GCF implicitly minimizes Gaussian curvature without the need to explicitly calculate the Gaussian curvature itself. GCF is highly efficient and is 20 times faster than the classical standard Gaussian curvature flow method. We present extensive experiments to demonstrate that GCF significantly outperforms state-of-the-art methods in minimizing Gaussian curvature, geometric feature preserving smoothing and mesh noise removal. This method can be used in a large range of applications that involve Gaussian curvature. Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：