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IEEE Symposium (IUS) Ultrasonics

作者： Pedro Vianna Merve Kulbay Pamela Boustros Sara-Ivana Calce Cassandra Larocque-Rigney Laurent Patry-Beaudoin Yi Hui Luo Muawiz Chaudary Samuel Kadoury Bich Nguyen Emmanuel Montagnon Eugene Belilovsky Guy Wolf Michaël Chassé An Tang Guy Cloutier Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM) Canada Laboratory of Biorheology and Medical Ultrasonics - CRCHUM Canada Clinical Laboratory of Image Processing - CRCHUM Canada Département de Radiologie Centre Hospitalier de l’Université de Montréal (CHUM) Canada Mila – Quebec AI Institute Canada École Polytechnique de Montréal Canada Department of Pathology CHUM Canada Concordia University Canada Department of Computer Sciences and Operations Research Université de Montréal Canada Institute of Biomedical Engineering Université de Montréal Canada

Early detection of nonalcoholic fatty liver disease (NAFLD) is crucial to avoid further complications. Ultrasound is often used for screening and monitoring of hepatic steatosis, however it is limited by the subjective interpretation of images. computer assisted diagnosis could aid radiologists to achieve objective grading, and artificial intelligence approaches have been tested across various medical applications. In this study, we evaluated the performance of a two-stage hepatic steatosis detection deep learning framework, with a first step of liver segmentation and a subsequent step of hepatic steatosis classification. We evaluated the models on internal and external datasets, aiming to understand the generalizability of the framework. In the external dataset, our segmentation model achieved a Dice score of 0.92 (95% CI: 0.78, 1.00), and our classification model achieved an area under the receiver operating characteristic curve of 0.84 (95% CI: 0.79, 0.89). Our findings highlight the potential benefits of applying artificial intelligence models in NAFLD assessment.

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Convolutional neural network for detection of pathological changes in MR images of the brain 6

Convolutional neural network for detection of pathological c...

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6th International Conference Information Technology and Nanotechnology. Session image processing and Earth Remote Sensing, ITNT-IPERS 2020

作者： Agafonova, Yulia Gaidel, Andrey Zelter, Pavel Kapishnikov, Aleksandr Faculty of Mathematics Samara National Research University Samara Russia Video Mining Laboratory Image Processing Systems Institute of RAS Branch of the FSRC "Crystallography and Photonics" RAS Samara Russia Radiology Department Clinic Samara State Medical University Samara Russia Department of Radiology and Radiation Therapy Samara State Medical University Samara Russia

At the present day, the problem is subsisting associated with a reliable diagnosis as soon as possible, especially in medicine in cases of diagnosis of neoplasms. The article discusses research method for the diagnosis of brain diseases in magnetic-resonance tomography images, based on deep learning. This paper presents a novel approach to solutions pattern classification, was formed the optimal architecture convolutional neural network. As a result of experimental studies, was undertake a study major characteristic of convolutional neural network. Through the use of this neural network architecture 95 % the images from the validation set were classified correctly. In addition, the results can be used as an intermediate result for further images analysis. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Magnetic resonance imaging

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Fuzzy Sandwich Control of Some Nonlinear Systems with a Number of Stochastically Occurring Impulses

Fuzzy Sandwich Control of Some Nonlinear Systems with a Numb...

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International Conference on Information Science and Technology (ICIST)

作者： Babatunde Oluwaseun Onasanya Yuming Feng Xiangguang Dai Wei Zhang School of Computer Science and Engineering Chongqing Three Gorges University Wanzhou Chongqing China Department of Mathematics University of Ibadan Ibadan Nigeria Three Gorges Institute of Artificial Intelligence Chongqing Three Gorges University Wanzhou Chongqing China Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology Chongqing Three Gorges University Wanzhou Chongqing China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Wanzhou Chongqing China

In this paper, rather than have a fixed constant matrix in the sandwich control of the nonlinear system, a fuzzy control matrix was used. In addition, simple inequalities that do not require additional computational time and skills in computer programming were used rather than the usual LMI. The result of this method is that the period T required in the control process is shorter and the control time is also reached much faster or reduced compared to the existing sandwich methods that used constant matrices. In other words, the control time of this method is shorter than the control time achieved when using constant matrices. Besides, this method also can be used to control more nonlinear chaotic systems such as Lorenz's, Rossler's and Chen's systems. Numerical simulations were used to demonstrate the effectiveness of this method.

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

arXiv

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arXiv 2025年

作者： Imran, Muhammad Krebs, Jonathan R. Sivaraman, Vishal Balaji Zhang, Teng Kumar, Amarjeet Ueland, Walker R. Fassler, Michael J. Huang, Jinlong Sun, Xiao Wang, Lisheng Shi, Pengcheng Rokuss, Maximilian Baumgartner, Michael Kirchhof, Yannick Maier-Hein, Klaus H. Isensee, Fabian Liu, Shuolin Han, Bing Nguyen, Bong Thanh Shin, Dong-Jin Ji-Woo, Park Choi, Mathew Uhm, Kwang-Hyun Ko, Sung-Jea Lee, Chanwoong Chun, Jaehee Kim, Jin Sung Zhang, Minghui Zhang, Hanxiao You, Xin Gu, Yun Pan, Zhaohong Liu, Xuan Liang, Xiaokun Tiefenthaler, Markus Almar-Munoz, Enrique Schwab, Matthias Kotyushev, Mikhail Epifanov, Rostislav Wodzinski, Marek Müller, Henning Qayyum, Abdul Mazher, Moona Niederer, Steven A. Wang, Zhiwei Yang, Kaixiang Ren, Jintao Korreman, Stine Sofia Gao, Yuchong Zeng, Hongye Zheng, Haoyu Zheng, Rui Yue, Jinghua Zhou, Fugen Liu, Bo Cosman, Alexander Liang, Muxuan Zhao, Chang Upchurch, Gilbert R. Ma, Jun Zhou, Yuyin Cooper, Michol A. Shao, Wei Department of Medicine University of Florida GainesvilleFL32611 United States Department of Surgery University of Florida GainesvilleFL32611 United States Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States Department of Computer Information Science and Engineering University of Florida GainesvilleFL32611 United States Shanghai Jiao Tong University Shanghai China Electronic & Information Engineering School Harbin Institute of Technology [Shenzhen Shenzhen China Division of Medical Image Computing German Cancer Research Center [DKFZ Heidelberg Germany CO. LTD Beijing China MedAI Seoul Korea Republic of Korea University Seoul Korea Republic of Department of Radiation Oncology Yonsei Cancer Center Heavy Ion Therapy Research Institute Yonsei University College of Medicine Seoul Korea Republic of Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Medical University of Innsbruck Innsbruck Austria Novosibirsk State University Novosibirsk Russia AGH University of Krakow Krakow Poland Sierre Switzerland University of Geneva Geneva Switzerland The Sense Innovation and Research Center Sion Switzerland National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan China Aarhus University Department of Clinical Medicine Nordre Palle Juul-Jensens Blvd. 11 Aarhus8200 Denmark Shanghaitech University Pudong Shanghai China Image Processing Center Beihang University Beijing China Department of Computer Science and Engineering University of California Santa CruzCA95064 United States Department of Biostatistics University of Florida GainesvilleFL32611 United States University Health Network ON Canada Agronomy Department University of F

Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across different branches and zones. Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods. To address this gap, we organized the AortaSeg24 MICCAI Challenge, introducing the first dataset of 100 CTA volumes annotated for 23 clinically relevant aortic branches and zones. This dataset was designed to facilitate both model development and validation. The challenge attracted 121 teams worldwide, with participants leveraging state-of-the-art frameworks such as nnU-Net and exploring novel techniques, including cascaded models, data augmentation strategies, and custom loss functions. We evaluated the submitted algorithms using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD), highlighting the approaches adopted by the top five performing teams. This paper presents the challenge design, dataset details, evaluation metrics, and an in-depth analysis of the top-performing algorithms. The annotated dataset, evaluation code, and implementations of the leading methods are publicly available to support further research. All resources can be accessed at https://***. © 2025, CC BY-NC-SA.

关键词： computerized tomography

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CoBe - Coded Beacons for Localization, Object Tracking, and SLAM Augmentation

CoBe - Coded Beacons for Localization, Object Tracking, and ...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Roman Rabinovich Ibrahim Jubran Aaron Wetzler Ron Kimmel Geometric Image Processing (GIP) Lab Computer Science Department Technion - Israel Institute of Technology Israel Robotics & Big Data (RBD) Lab Computer Science Department University of Haifa Israel

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents a novel beacon light coding protocol, which enables fast and accurate identification of the beacons in an image. The protocol is provably robust to a predefined set of detection and decoding errors and does not require any synchronization between the beacons themselves and the optical sensor. A detailed guide is then given for developing an optical tracking and localization system, which is based on the suggested protocol and readily available hardware. Such a system operates either as a standalone system for recovering the six degrees of freedom of fast moving objects, or integrated with existing SLAM pipelines providing them with error-free and easily identifiable landmarks. Based on this guide, we implemented a low-cost positional tracking system which can run in real-time on an IoT board. We evaluate our system's accuracy and compare it to other popular methods which utilize the same optical hardware, in experiments where the ground truth is known. A companion video containing multiple real-world experiments demonstrates the accuracy, speed, and applicability of the proposed system in a wide range of environments and real-world tasks. Open source code is provided to encourage further development of low-cost localization systems integrating the suggested technology at its navigation core.

关键词： Optical sensors Robustness Simultaneous localization and mapping Protocols Decoding Adaptive optics Optical imaging

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： image reconstruction

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Reflexivity of Partitions Induced by Weighted Poset Metric and Combinatorial Metric

arXiv

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arXiv 2022年

作者： Xu, Yang Kan, Haibin Han, Guangyue Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Department of Mathematics Faculty of Science The University of Hong Kong Pokfulam Road Hong Kong Hong Kong Shanghai Engineering Research Center of Blockchain Shanghai200433 China Yiwu Research Institute of Fudan University Zhejiang Yiwu City322000 China

Let H be the Cartesian product of a family of finite abelian groups. Via a polynomial approach, we give sufficient conditions for a partition of H induced by weighted poset metric to be reflexive, which also become necessary for some special cases. Moreover, by examining the roots of the Krawtchouk polynomials, we establish non-reflexive partitions of H induced by combinatorial metric. When H is a vector space over a finite field F, we consider the property of admitting MacWilliams identity (PAMI) and the MacWilliams extension property (MEP) for partitions of H. With some invariance assumptions, we show that two partitions of H admit MacWilliams identity if and only if they are mutually dual and reflexive, and any partition of H satisfying the MEP is in fact an orbit partition induced by some subgroup of Aut F(H), which is necessarily reflexive. As an application of the aforementioned results, we establish partitions of H induced by combinatorial metric that do not satisfy the MEP, which further enable us to provide counter-examples to a conjecture proposed by Pinheiro, Machado and Firer in [39]. © 2022, CC BY.

关键词： Vector spaces

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Evaluation of the mechanism damage using model of vibration signal as a periodically correlated random process

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Procedia Structural Integrity 2022年 36卷 122-129页

作者： Ihor Javorskyj Roman Yuzefovych Oleh Lychak Ivan Matsko Pavlo Semenov Karpenko Physico-Mechanical Institute of National Academy of Sciences of Ukraine Department of methods and facilities for acquisition and processing diagnostic signals Naukova Str. 5 79060 Lviv Ukraine Bydgoszcz University of Sciences and Technology Institute of Telecommunication and Computer Sciences Al. Prof. S. Kaliskiego 7 85796 Bydgoszcz Poland Lviv Polytechnic National University Department of Applied Mathematics Bandera Str. 12 79000 Lviv Ukraine Odessa National Maritime University Department of hoisting and Transport Mashines and Engineering of Port Technological Equipment Mechnikova Str. 34 65029 Odessa Ukraine

The results of the processing of the wind turbine gearbox vibration signals are presented. The least square estimators of the periods of the deterministic part of the vibration signal and time variations power of stochastic part are obtained. The amplitude spectra of the deterministic and stochastic oscillations for different degrees of the gearbox damage are analyzed. The covariance structure of stochastic part of vibration was analyzed. The efficient indicator for practical evaluation of gear teeth damage, formed on the basis of amplitude spectra was proposed.

关键词： vibration signal periodically correlated random processes gear pair damage indicator

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PALMNUT: An enhanced proximal alternating linearized minimization algorithm with application to separate regularization of magnitude and phase

arXiv

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arXiv 2020年

作者： Liu, Yunsong Haldar, Justin P. Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States

We introduce a new algorithm for complex image reconstruction with separate regularization of the image magnitude and phase. This optimization problem is interesting in many different image reconstruction contexts, although is nonconvex and can be difficult to solve. In this work, we first describe a novel implementation of the previous proximal alternating linearized minization (PALM) algorithm to solve this optimization problem. We then make enhancements to PALM, leading to a new algorithm named PALMNUT that combines the PALM together with Nesterov’s momentum and a novel approach that relies on uncoupled coordinatewise step sizes derived from coordinatewise Lipschitz-like bounds. Theoretically, we establish that a version of PALMNUT (without Nesterov’s momentum) monotonically decreases the objective function, leading to guaranteed convergence in many cases of interest. Empirical results obtained in the context of magnetic resonance imaging demonstrate that PALMNUT has computational advantages over common existing approaches like alternating minimization. Although our focus is on the application to separate magnitude and phase regularization, we expect that the same approach may also be useful in other nonconvex optimization problems with similar objective function structure. © 2020, CC BY-NC-SA.

关键词： Linearization

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