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arXiv 2024年

作者： Jin, Rui Li, Derun Xiang, Dehui Zhang, Lei Zhou, Hailing Shi, Fei Zhu, Weifang Cai, Jing Peng, Tao Chen, Xinjian School of Future Science and Engineering Soochow University Suzhou China Department of Urology Peking University First Hospital Xicheng District Beijing100034 China MIPAV Lab The School of Electronic and Information Engineering Soochow University Jiangsu China Graduate Program of Medical Physics and Data Science Research Center Duke Kunshan University Jiangsu Kunshan215316 China Department of Mechanical Engineering and Product Design Engineering Swinburne University of Technology Melbourne Australia Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States The State Key Laboratory of Radiation Medicine and Protection Soochow University Jiangsu China

Prostate cancer represents a major threat to health. Early detection is vital in reducing the mortality rate among prostate cancer patients. One approach involves using multi-modality (CT, MRI, US, etc.) computer-aided diagnosis (CAD) systems for the prostate region. However, prostate segmentation is challenging due to imperfections in the images and the prostate’s complex tissue structure. The advent of precision medicine and a significant increase in clinical capacity have spurred the need for various data-driven tasks in the field of medical imaging. Recently, numerous machine learning and data mining tools have been integrated into various medical areas, including image segmentation. This article proposes a new classification method that differentiates supervision types, either in number or kind, during the training phase. Subsequently, we conducted a survey on artificial intelligence (AI)-based automatic prostate segmentation methods, examining the advantages and limitations of each. Additionally, we introduce variants of evaluation metrics for the verification and performance assessment of the segmentation method and summarize the current challenges. Finally, future research directions and development trends are discussed, reflecting the outcomes of our literature survey, suggesting high-precision detection and treatment of prostate cancer as a promising avenue. Impact Statement — Prostate cancer remains a significant health threat, necessitating early detection to reduce mortality rates. Current diagnostic approaches, particularly in medical imaging, face challenges in accurate prostate segmentation due to the organ's complex structure and imaging imperfections. Our review paper delves into AI-based automatic segmentation methods across various imaging modalities (CT, MRI, US), emphasizing their strengths, weaknesses, and the specific challenges they address. By classifying segmentation techniques based on supervision levels, we provide a comprehensive analys

关键词： Computer aided diagnosis

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AImotion Challenge Results: a Framework for AirSim Autonomous Vehicles and Motion Replication

AImotion Challenge Results: a Framework for AirSim Autonomou...

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Computers and Automation (CompAuto), International Conference on

作者： Bruno J. Souza Lucas C. de Assis Dominik Rößle Roberto Z. Freire Daniel Cremers Torsten Schön Munir Georges Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Control and Automation Eng. Department Pontifical Catholic University of Parana (PUCPR) Curitiba Brazil Technische Hochschule Ingolstadt AImotion Bavaria Institute Ingolstadt Germany Department of Informatics Technical University of Munich Munich Germany

ISBN: (纸本)9781665481953

The use of simulation environments is becoming more significant in the development of autonomous cars, as it allows for the simulation of high-risk situations while also being less expensive. In this paper, we presented a framework that allows the creation of an autonomous vehicle in the AirSim simulation environment and then transporting the simulated movements to the TurtleBot. To maintain the car in the correct direction, computer vision techniques such as object detection and lane detection were assumed. The vehicle's speed and steering are both determined by Proportional-Integral-Derivative (PID) controllers. A virtual personal assistant was developed employing natural language processing to allow the user to interact with the environment, providing movement instructions related to the vehicle's direction. Additionally, the conversion of the simulator movements for a robot was implemented to test the proposed system in a practical experiment. A comparison between the real position of the robot and the position of the vehicle in the simulated environment was considered in this study to evaluate the performance of the algorithms.

关键词： Computer vision Lane detection Robot kinematics Atmospheric modeling Computational modeling Virtual environments Natural language processing

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A Map Building and Sharing Framework for Multiple UAV Systems

A Map Building and Sharing Framework for Multiple UAV System...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Antonio Silva Maik Basso Paulo Mendes Denis Rosá rio Eduardo Cerqueira Bruno J. G. Praciano Joã o Paulo J. da Costa Edison Pignaton de Freitas Institute of Informatics Federal University of Rio Grande do Sul Brazil Graduate Program in Electrical Engineering Federal University of Rio Grande do Sul Brazil Airbus Taufkirchen Germany Federal University of Par&#x00E1 (UFPA) Bel&#x00E9 m Brazil University of Brasilia (UnB) Brasilia Brazil Hochschule Hamm-Lippstadt (HSHL) Hamm Germany

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

With the popularity of Unmanned Aerial Vehicles (UAVs), there is an increasing demand for autonomous navigation applications, which require the use of high-definition mapping techniques of the environments. However, in highly dynamic environments, maps need to be updated regularly, which has an impact on the path planning algorithms used by the autonomous navigation system. A deterministic energy-efficient solution for a straightforward path in the path planning is computationally exhaustive. To overcome this limitation, recent solutions based on edge computing aim to provide safe and energy-efficient solutions to assist UAVs in autonomous navigation. This paper proposes an energy-efficient map building and sharing framework for multiple UAV systems. The proposed system uses edge servers to merge a global map that incorporates all Simultaneous Localization and Mapping updates, providing UAVs with consistent maps that are efficiently updated. The proposed system is evaluated and tested in laboratory, and field experiments. The field tests revealed that the object detection and classification model obtained a median efficiency rate of around 95%, even with a median packet loss rate between 25% and 35%. For the benefit of the community, we make public the source code.

关键词： Simultaneous localization and mapping Heuristic algorithms Buildings Packet loss Autonomous aerial vehicles Energy efficiency Path planning

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Radiotherapy delivery error detection with electronic portal imaging device (EPID)-based in vivo dosimetry

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Chinese Medical Journal 2023年第8期136卷 998-1000页

作者： Xueying Yang Lisheng Geng David Huang Kaiwen Li Hongqing Zhuang Jing Cai Ruijie Yang School of Physics Beihang UniversityBeijing 102206China Department of Radiation Oncology Peking University Third HospitalBeijing 100191China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China Medical Physics Graduate Program Duke Kunshan UniversityKunshanJiangsu 215316China Medical Management Department CAS Ion Medical Technology Co.Ltd.Beijing 100190China Department of Health Technology and Informatics The Hong Kong Polytechnic UniversityHong Kong 999077China

To the Editor:Due to the highly complex nature of intensity-modulated radiotherapy(IMRT)planning and delivery,patient specific quality assurance(PSQA)should be implemented to assure the reliability of treatment delive... 详细信息

关键词： IMRT treatment vivo

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X-RIM: Extreme Recurrent Independent Mechanisms for Noise-resistant and Interpretable Stroke Risk Prediction

X-RIM: Extreme Recurrent Independent Mechanisms for Noise-re...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yi-Hsien Hsieh Jung-Chi Hsu Che Lin Lian-Yu Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Department of Internal Medicine Division of Cardiology Fu Jen Catholic University Hospital New Taipei City Taiwan Department of Internal Medicine and Cardio-Vascular Center Division of Cardiology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Computational and Systems Biology and Center for Biotechnology NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan Department of Internal Medicine NTU Taipei Taiwan

Stroke is a leading cause of mortality and long-term disability worldwide. An accurate stroke risk prediction is crucial for its early detection and prevention. Using deep learning to exploit patients’ time-series electronic health records (EHRs) has been shown as a promising and efficient solution for such a prediction. Although time-series data could be more informative than a single cross-section in time, real-world time-series EHRs usually have a significantly high missing rate due to irregular patient visits. This could undermine sequential data’s benefits unless a proper deep-learning model design is adopted. Furthermore, deep models have long been challenged for their interpretability, which is especially crucial for medical applications. In this study, we propose an extreme design based on the concept of recurrent independent mechanisms (RIM), termed extreme RIM (X-RIM). With no need for imputation, X-RIM utilizes the information of each input feature’s temporal records through independent recurrent modules. Experiments on real-world data from the National Taiwan University Hospital showed that, in terms of the area under the precision-recall curve (AUPRC), the area under the receiver-operating characteristics curve (AUROC), and Youden Index, X-RIM (AUPRC: 0.210; AUROC: 0.764; Youden: 0.373) outperformed the classic risk score CHA 2 DS 2 -VASc (AUPRC: 0.103; AUROC: 0.650; Youden: 0.223) and other benchmarks in stroke risk prediction. Additional experiments also indicate that individual feature contributions to a prediction could be evaluated intuitively under X-RIM’s independent structure to enhance interpretability.

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Improving Land Cover Segmentation Using Multispectral Dataset

Improving Land Cover Segmentation Using Multispectral Datase...

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International Conference on informatics and Computing (ICIC)

作者： Herlawati Herlawati Rahmadya Trias Handayanto Yaya Heryadi Edi Abdurachman Haryono Soeparno Edy Irwansyah Eka Miranda Informatics Universitas Bhayangkara Jakarta Raya Jakarta Indonesia Computer Engineering Universitas Islam 45 Bekasi Indonesia Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Trisakti Institute of Transportation and Logistic Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

Computer vision has been used in many areas such as medical, transportation, military, geography, etc. The fast development of sensor devices inside camera and satellite provides not only red-greed-blue (RGB) images but also multispectral dataset with some channels including RGB, infrared, short-wave, and thermal wave. Most of the dataset is panchromatic (black and white) and RGB, for example Google Map and other satellite-based map applications. This study examines the effects of multispectral dataset for semantic segmentation of land cover. The comparison between RGB with band 2 to band 7 of Landsat 8 Satellite shows an improvement of accuracy from 90.283 to 94.473 for U-Net and from 91.76 to 95.183 for DeepLabV3+. In addition, this research also compares two well-known semantic segmentation methods, namely U-Net and DeepLabV3+, that shown that DeepLabV3+ outperformed U-Net regarding to speed and accuracy. Testing was conducted in the Karawang Regency area, West Java, Indonesia.

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Realistic river image synthesis using deep generative adversarial networks

arXiv

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arXiv 2020年

作者： Gautam, Akshat Sit, Muhammed Demir, Ibrahim School of Engineering and Applied Sciences Columbia University Interdisciplinary Graduate Program in Informatics University of Iowa Department of Civil and Environmental Engineering University of Iowa

In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support modeling and analysis in surface water estimation, river meandering, wetland loss, and other hydrological research studies. First, we have created an extensive repository of overhead river images to be used in training. Second, we incorporated the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution to generate high quality (i.e., 1024x1024) synthetic river imagery. With simpler GAN architectures, difficulties arose in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seemed to significantly reduce. The results presented in this study show great promise in generating high-quality images and capturing the details of river structure and flow to support hydrological research, which often requires extensive imagery for model performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Rivers

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Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

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Scientific Reports 2023年第1期13卷 1-10页

作者： Kuo, Tzu-Yun Wang, Jen-Hung Huang, Yung-Wen Sung, Ting-Yi Chen, Ching-Tai Department of Biochemical Science and Technology National Taiwan University Taipei 10617 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Statistical Science Academia Sinica Taipei 11529 Taiwan Institute of Information Science Academia Sinica Taipei 11529 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Bioinformatics and Biomedical Engineering Asia University Taichung 41354 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms;its executable files, user manual, and sample data sets are freely available at https://***/comics/Software_*** . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline. © 2023, Springer Nature Limited.

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Internet of Things-based Hydroponic: Literature Review

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Journal of Physics: Conference Series 2021年第1期2111卷

作者： Alan Hadinata Mashoedah Graduate Program of Electronics and Informatics Engineering Education State University of Yogyakarta

Hydroponics is a method of growing crops without using soil, with the benefits of controlling the environment and nutrients, conserving water, and reducing labor. The applied technology is used to improve results that have consistency. This research was conducted with the aim of knowing the IoT devices and platforms used in the development of IoT-based hydroponics. Data obtained from the website *** and *** from 2016 to 2021. This study is a literature review using the PRISMA method. This method is used for literature review using a systematic and structured basic framework. The results of this study indicate that the variables measured are Temperature, Potential Hydrogen (pH), Total Dissolve Solid, Water Temperature, Humidity, UV, Carbon Dioxide, Soil Moisture and Electrical Conductivity. The device used is the ESP8266, Arduino, and Raspberry PI with the MySQL, Thingspeak, Firebase, Domoticz, and Wyliodrin IoT Platforms.

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Tuning the Size and Stiffness of Inflatable Particles

arXiv

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arXiv 2025年

作者： Pashine, Nidhi Wang, Dong Baines, Robert Goyal, Medha Shattuck, Mark D. O’Hern, Corey S. Kramer-Bottiglio, Rebecca Department of Mechanical Engineering Yale University New HavenCT06520 United States Department of Physics BioInspired Institute Syracuse University SyracuseNY13244 United States Department of Physics Benjamin Levich Institute City College of New York New York CityNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Biomedical Informatics Yale University New HavenCT06520 United States

We describe size-varying cylindrical particles made from silicone elastomers that can serve as building blocks for robotic granular materials. The particle size variation, which is achieved by inflation, gives rise to changes in stiffness under compression. We design and fabricate inflatable particles that can become stiffer or softer during inflation, depending on key parameters of the particle geometry, such as the ratio of the fillet radius to the wall thickness, r/t. We also conduct numerical simulations of the inflatable particles and show that they only soften during inflation when localization of large strains occurs in the regime r/t → 0. This work introduces novel particle systems with tunable size and stiffness that can be implemented in numerous soft robotic applications. © 2025, CC BY.

关键词： Stiffness

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