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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

A Novel Big Data-Enabled Approach, Individualizing and Optimizing Brain Disorder Rehabilitation

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Studies in Big Data

Studies in Big Data 2019年 42卷 101-127页

作者： Janatova, Marketa Uller, Miroslav Stepankova, Olga Brezany, Peter Lenart, Marek Prague Czech Republic Robotics and Machine Perception Department CIIRC CTU Prague Czech Republic Biomedical Engineering and Assistive Technologies Department CIIRC CTU Prague Czech Republic Faculty of Computer Science University of Vienna Vienna Austria SIX Research Group Brno University of Technology Brno Czech Republic

Brain disorders occur when our brain is damaged or negatively influenced by injury, surgery, or health conditions. This chapter shows how the combination of novel biofeedback-based treatments producing large data sets with Big Data and Cloud-Dew Computing paradigms can contribute to the greater good of patients in the context of rehabilitation of balance disorders, a significant category of brain damage impairments. The underlying hypothesis of the presented original research approach is that detailed monitoring and continuous analysis of patient´s physiological data integrated with data captured from other sources helps to optimize the therapy w.r.t. the current needs of the patient, improves the efficiency of the therapeutic process, and prevents patient overstressing during the therapy. In the proposed application model, training built upon two systems, Homebalance—a system enabling balance training and Scope—a system collecting physiological data, is provided both in collaborating rehabilitation centers and at patient homes. The preliminary results are documented using a case study confirming that the approach offers a viable way towards the greater good of a patient. © Springer International Publishing AG, part of Springer Nature 2019.

关键词： Patient rehabilitation

AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking

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arXiv 2024年

作者： Li, Wenxuan Qu, Chongyu Chen, Xiaoxi Bassi, Pedro R.A.S. Shi, Yijia Lai, Yuxiang Yu, Qian Xue, Huimin Chen, Yixiong Lin, Xiaorui Tang, Yutong Cao, Yining Han, Haoqi Zhang, Zheyuan Liu, Jiawei Zhang, Tiezheng Ma, Yujiu Wang, Jincheng Zhang, Guang Yuille, Alan Zhou, Zongwei Department of Computer Science Johns Hopkins University United States Department of Bioengineering University of Illinois Urbana-Champaign United States Alma Mater Studiorum University of Bologna Italy Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Italy LKS Faculty of Medicine The University of Hong Kong Hong Kong Department of Computer Science Southeast University China Department of Radiology Southeast University Zhongda Hospital China Department of Medical Oncology The First Hospital of China Medical University China The Second Clinical College China Medical University China Department of Mechanical Engineering The Laboratory of Computational Sensing and Robotics Johns Hopkins University United States Center of Reproductive Medicine Department of Obstetrics and Gynecology Shengjing Hospital of China Medical University China Radiology Department The First Affiliated Hospital School of Medicine Zhejiang University China Department of Health Management The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital China Shandong Engineering Research Center of Health Management China Shandong Institute of Health Management China

We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-dimensional CT volumes sourced from 112 hospitals across diverse populations, geographies, and facilities. AbdomenAtlas provides 673K high-quality masks of anatomical structures in the abdominal region annotated by a team of 10 radiologists with the help of AI algorithms. We start by having expert radiologists manually annotate 22 anatomical structures in 5,246 CT volumes. Following this, a semi-automatic annotation procedure is performed for the remaining CT volumes, where radiologists revise the annotations predicted by AI, and in turn, AI improves its predictions by learning from revised annotations. Such a large-scale, detailed-annotated, and multi-center dataset is needed for two reasons. Firstly, AbdomenAtlas provides important resources for AI development at scale, branded as large pre-trained models, which can alleviate the annotation workload of expert radiologists to transfer to broader clinical applications. Secondly, AbdomenAtlas establishes a large-scale benchmark for evaluating AI algorithms—the more data we use to test the algorithms, the better we can guarantee reliable performance in complex clinical scenarios. An ISBI & MICCAI challenge named BodyMaps: Towards 3D Atlas of Human Body was launched using a subset of our AbdomenAtlas, aiming to stimulate AI innovation and to benchmark segmentation accuracy, inference efficiency, and domain generalizability. We hope our AbdomenAtlas can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community. Codes, models, and datasets are available at https://***/dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Time-of-use pricing for energy storage investment

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arXiv 2021年

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun MIT Energy Initiative Massachusetts Institute of Technology CambridgeMA02139 United States Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University MelbourneVIC3800 Australia School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

Time-of-use (ToU) pricing is widely used by the electricity utility to shave peak load. Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals. However, without considering the implication on energy storage investment, an improperly designed ToU pricing scheme may lead to significant welfare loss, especially when users over-invest the storage, which leads to new energy consumption peaks. In this paper, we will study how to design a social-optimum ToU pricing scheme by explicitly considering its impact on storage investment. We model the interactions between the utility and users as a two-stage optimization problem. To resolve the challenge of asymmetric information due to users’ private storage cost, we propose a ToU pricing scheme based on different storage types and the aggregate demand per type. Each user does not need to reveal his private cost information. We can further compute the optimal ToU pricing with only a linear complexity. Simulations based on real-world data show that the suboptimality gap of our proposed ToU pricing, compared with the social optimum achieved under complete information, is less than 5%. © 2021, CC BY.

关键词： Investments

Study of voice data communication using network streams on dataflow programming 2

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Study of voice data communication using network streams on d...

2nd International Conference on Engineering and Technology for Sustainable Development, ICET4SD 2017

作者： Prasetio, Barlian Henryranu Syauqy, Dahnial Maulana, Rizal Setyawan, Gembong Edhi System and Robotics Lab Faculty of Computer Science University of Brawijaya Indonesia

In this paper, we implement a voice data communications between sender and receiver through Wi-Fi. The system was designed and implemented using network stream protocol which was developed by National Instruments on Lab-VIEW between two devices. The device which acts as the sender was My-RIO board and the receiver device was Personal computer. The voice data communication system was tested by observing the effect of various buffer sizes on the throughput, latency and CPU usage. The result showed that the larger the buffer size being used, the throughput values were also getting bigger. However, particular buffer size made the throughput became saturated. The change on the buffer size also directly affects CPU usage. It seemed that the buffer size did not affect latency significantly. It showed that the latency was stable with an average of 13 milliseconds on voice data transmission. © The Authors, published by EDP sciences, 2018.

关键词： Convolutional codes

A Data logger for educational purposes of a laboratory chemical reactor: an IoT approach

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A Data logger for educational purposes of a laboratory chemi...

Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： José Lima Thadeu Brito Olga Ferreira Maria J. Afonso Vítor H. Pinto José A. Carvalho Paulo Costa Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Politécnico de Bragança Bragança Portugal Laboratório Para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragança Bragança Portugal Centre for Robotic in Industry and Intelligent Systems Institute for Systems and Computer Engineering Technology and Science (INESC-TEC) Porto Portugal CeDRI SusTEC INESC-TEC Faculty of Engineering of University of Porto (FEUP) Porto Portugal SusTEC ESTiG Instituto Politécnico de Bragança Bragança Portugal ARISE & ECE Dept. Research Center for Systems and Technologies (SYSTEC) FEUP CeDRI SusTEC INESC-TEC FEUP

This paper presents the development of an acquisition system and data logger from an existing set of three continuous stirred-tank reactors in series. The reactors are currently used in chemical engineering educational laboratories to perform kinetic and tracer experiments. In this sense, to accomplish the store data process, the volumetric flow rate and the concentration of tracer, reactants and/or products of the reaction must be acquired as a function of time. In the original experimental setup, only the signal conditioning system was operational, while the acquisition, visualization, and control systems were obsolete and damaged. Thus, a new system composed of an interface and real-time acquisition data is proposed alongside preserving the main reactor structure. A graphical user interface and the automation of the various actuators were developed based on worldwide usage and low cost, respectively. This system, based on a common 8-bit microcontroller and an application developed in Lazarus, allows the storage of the acquired data in a time-series database. In this way, students can analyze the results later or in real time. Moreover, remote access allows controlling the reactor and getting data by the Internet of Things (IoT) resources. Additionally, the proposed system using IoT allows data to be shared with the community as a dataset.

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Parameter Estimation of Nonlinearly Parameterized Regressions: Application to System Identification and Adaptive Control

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IFAC-PapersOnLine 2020年第2期53卷 1206-1212页

作者： Romeo Ortega Vladislav Gromov Emmanuel Nuño Anton Pyrkin Jose Guadalupe Romero Laboratoire des Signaux et Systémes Supélec CNRS-SUPELEC Gif-sur-Yvette France Faculty of Control Systems and Robotics ITMO University Saint Petersburg Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Department of Computer Science CUCEI University of Guadalajara Guadalajara Mexico Department of Digital Systems ITAM Mexico City Mexico

We propose a solution to the problem of parameter estimation of nonlinearly parameterized regressions—continuous or discrete time—and apply it for system identification and adaptive control. We restrict our attention to parameterizations that can be factorized as the product of two functions, a measurable one and a nonlinear function of the parameters to be estimated. Another feature of the proposed estimator is that parameter convergence is ensured without a persistency of excitation assumption. It is assumed that, after a coordinate change, some of the elements of the transformed function satisfy a monotonicity condition. The proposed estimators are applied to design identifiers and adaptive controllers for nonlinearly parameterized systems, which are traditionally tackled using overparameterization and assuming persistency of excitation.

关键词： Adaptive control systems identification nonlinear control

Deep Learning Techniques for Future Intelligent Cross-Media Retrieval

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arXiv 2020年

作者： ur Rehman, Sadaqat Waqas, Muhammad Tu, Shanshan Koubaa, Anis ur Rehman, Obaid Ahmad, Jawad Hanif, Muhammad Han, Zhu Department of Computer Science Namal Institute - an associated college of University of Bradford United Kingdom Faculty of Information Technology Beijing University of Technology Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Robotics and Internet-of-Things research lab Department of Computer Science Prince Sultan University R&D Gai-tech Robotics China CISTER/INESC TEC and ISEP-IPP Porto Portugal Department of EE Sarhad University of Science and IT Pakistan Department of Computer Science Edinburgh Napier University United Kingdom Department of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi23460 Pakistan Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

With the advancement in technology and the expansion of broadcasting, cross-media retrieval has gained much attention. It plays a significant role in big data applications and consists in searching and finding data from different types of media. In this paper, we provide a novel taxonomy according to the challenges faced by multi-modal deep learning approaches in solving cross-media retrieval, namely: representation, alignment, and translation. These challenges are evaluated on deep learning (DL) based methods, which are categorized into four main groups: 1) unsupervised methods, 2) supervised methods, 3) pairwise based methods, and 4) rank based methods. Then, we present some well-known cross-media datasets used for retrieval, considering the importance of these datasets in the context in of deep learning based cross-media retrieval approaches. Moreover, we also present an extensive review of the state-of-the-art problems and its corresponding solutions for encouraging deep learning in cross-media retrieval. The fundamental objective of this work is to exploit Deep Neural Networks (DNNs) for bridging the "media gap", and provide researchers and developers with a better understanding of the underlying problems and the potential solutions of deep learning assisted cross-media retrieval. To the best of our knowledge, this is the first comprehensive survey to address cross-media retrieval under deep learning methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

Implementation of IoT Concept for Early Diagnostic of Subacute Rumen Acidosis in Cows

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Implementation of IoT Concept for Early Diagnostic of Subacu...

Mediterranean Conference on Embedded Computing (MECO)

作者： Anatolijs Zabasta Nadezda Kunicina Uldis Grunde Janis Judvaitis Ilga Sematovica Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Robotics and Machine Perception laboratory Institute of Electronics and Computer Science Riga Latvia Cyber-Physical Systems laboratory Institute of Electronics and Computer Science Riga Latvia Faculty of Veterinary Medicine of Latvia University of Life Sciences and Technologies Jelgava Latvia

ISBN: (数字)9781728169491

ISBN: (纸本)9781728169507

Highly productive dairy cow' ration under intensive production conditions causes the development of subacute rumen acidosis (SARA). In this paper we discuss a reticulo-ruminal long-acting cyber-physical diagnostic system' prototype, which applies Internet of Things (IoT) for monitoring rumen parameters of cows. The new diagnostic system architecture includes, reticulo-ruminal bolus with pH and temperature sensors, a microcontroller, a radio transmitter and a power supply module. The system includes gateways for data collection from boluses, an MQTT (Message Queuing Telemetry Transport) broker, a web server and a database. The diagnostic system' prototype provides timely data on cow health status to the users, so they can evaluate the cow' health status and implement further actions. Therefore, the use of diagnostic system provides opportunity to increase cow productivity, longevity and to save maintenance cost of milk farms. The results of the first stage of the research are discussed in this paper.

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