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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

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Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach

arXiv

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arXiv 2024年

作者： Liu, Xian-Xian Xu, Mingkun Wei, Yuanyuan Qin, Huafeng Song, Qun Fong, Simon Tien, Feng Luo, Wei Gao, Juntao Zhang, Zhihua Siu, Shirley The Department of Computer and Information Science University of Macau 999078 China Guangdong Institute of Intelligence Science and Technology Zhuhai519031 China Department of Biomedical Engineering The Chinese University of Hong Kong Shatin999077 China Department of Neurology David Geffen School of Medicine University of California Los AngelesCA90095 United States The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing400067 China Institute of Artificial Intelligence Chongqing Technology and Business University Chongqing400067 China Hebei Key Laboratory of Medical Data Science Institute of Biomedical Informatics School of Medicine Hebei University of Engineering Hebei Province Handan056038 China The director of the Institute of Clinical Medicine The First People’s Hospital of Foshan Guangzhou510060 China Tsinghua University California Beijing100084 China Beijing Institute of Genomics Chinese Academy of Sciences Beijing100101 China Centre for Artificial Intelligence Driven Drug Discovery Macao Polytechnic University Rua de Luís Gonzaga Gomes 999078 China

Timely and precise classification and segmentation of gastric bleeding in endoscopic imagery are pivotal for the rapid diagnosis and intervention of gastric complications, which is critical in life-saving medical procedures. Traditional methods grapple with the challenge posed by the indistinguishable intensity values of bleeding tissues adjacent to other gastric structures. Our study seeks to revolutionize this domain by introducing a novel deep learning model, the Dual Spatial Kernelized Constrained Fuzzy C-Means (Deep DuS-KFCM) clustering algorithm. This Hybrid Neuro-Fuzzy system synergizes Neural Networks with Fuzzy Logic to offer a highly precise and efficient identification of bleeding regions. Implementing a two-fold coarse-to-fine strategy for segmentation, this model initially employs the Spatial Kernelized Fuzzy C-Means (SKFCM) algorithm enhanced with spatial intensity profiles and subsequently harnesses the state-of-the-art DeepLabv3+ with ResNet50 architecture to refine the segmentation output. Through extensive experiments across mainstream gastric bleeding and red spots datasets, our Deep DuS-KFCM model demonstrated unprecedented accuracy rates of 87.95%, coupled with a specificity of 96.33%, outperforming contemporary segmentation methods. The findings underscore the model’s robustness against noise and its outstanding segmentation capabilities, particularly for identifying subtle bleeding symptoms, thereby presenting a significant leap forward in medical image processing. © 2024, CC BY.

关键词： Medical image processing

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A decision model for decentralized autonomous organization platform selection: three industry case studies

arXiv

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arXiv 2021年

作者： Baninemeh, Elena Farshidi, Siamak Jansen, Slinger Department of Information and Computer Science Utrecht University Utrecht Netherlands Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Information and Computer Science Utrecht University Utrecht Netherlands Department of Software Engineering Lappeenranta University of Technology Lappeenranta Finland

Context: Decentralized autonomous organizations as a new form of online governance are collections of smart contracts deployed on a blockchain platform that intercede groups of people. A growing number of Decentralized Autonomous Organization Platforms, such as Aragon and Colony, have been introduced in the market to facilitate the development process of such organizations. Selecting the best fitting platform is challenging for the organizations, as a significant number of decision criteria, such as popularity, developer availability, governance issues, and consistent documentation of such platforms, should be considered. Additionally, decision-makers at the organizations are not experts in every domain, so they must continuously acquire volatile knowledge regarding such platforms and keep themselves updated. Accordingly, a decision model is required to analyze the decision criteria using systematic identification and evaluation of potential alternative solutions for a development project. Method: We have developed a theoretical framework to assist software engineers with a set of Multi-Criteria Decision-Making problems in software production. This study presents a decision model as a Multi-Criteria Decision-Making problem for the decentralized autonomous organization platform selection problem to capture knowledge regarding such platforms and concepts systematically. Results: We conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting decision-makers. The case study participants declared that the decision model provides significantly more insight into their selection process and reduces the cost of the decision-making process. Conclusion: We find that with empirical evidence from the case studies, that decision-makers can make more rational, efficient, and effective decisions with the decision model when they meet their requirements and priori

关键词： Decision support systems

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Research on reversible functions having component functions with specified properties: An overview 12th

Research on reversible functions having component functions ...

引用

12th International Conference on Reversible Computation, RC 2020

作者： Kerntopf, Pawel Moraga, Claudio Podlaski, Krzysztof Stanković, Radomir Institute of Computer Science Warsaw University of Technology Warsaw Poland Faculty of Computer Science Technical University of Dortmund Dortmund Germany Faculty of Physics and Applied Informatics University of Lódź Lódź Poland Department of Computer Science Faculty of Electronic Engineering University of Niš Niš Serbia

ISBN: (纸本)9783030473600

In the traditional logic synthesis, different classifications of non-reversible Boolean functions have found many applications. Recently, some attempts to deal with classifications of reversible functions have been published. In this paper, an overview of our results towards constructing a new classification of reversible functions is presented. These results were obtained due to our discussions during two Short Term Scientific Missions (STSMs) as well as during our further research in the framework of COST Action IC1405 "Reversible Computation - Extending Horizons of Computing" and were published in five papers. © The Author(s) 2020.

关键词： Logic Synthesis

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COMPUTATION-EFFICIENT ERA: A COMPREHENSIVE SURVEY OF STATE SPACE MODELS IN MEDICAL IMAGE ANALYSIS

arXiv

引用

arXiv 2024年

作者： Heidari, Moein Kolahi, Sina Ghorbani Karimijafarbigloo, Sanaz Azad, Bobby Bozorgpour, Afshin Hatami, Soheila Azad, Reza Diba, Ali Bagci, Ulas Merhof, Dorit Hacihaliloglu, Ilker School of Biomedical Engineering University of British Columbia BC Canada Department of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University Brookings United States Faculty of Mechanical Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Lunit Inc. Amsterdam Netherlands Department of Radiology Northwestern University Chicago United States Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Radiology Department of Medicine University of British Columbia BC Canada

Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emergence of transformers has altered this paradigm due to their superior performance. Built upon these advances, Vision Transformers (ViTs) have conjoined Convolutional Neural Networks (CNNs) as two leading foundational models for learning visual representations. However, transformers are hindered by the O(N2) complexity of their attention mechanisms, while CNNs lack global receptive fields and dynamic weight allocation. State Space Models (SSMs), specifically the Mamba model with selection mechanisms and hardware-aware architecture, have garnered immense interest lately in sequential modeling and visual representation learning, challenging the dominance of transformers by providing infinite context lengths and offering substantial efficiency maintaining linear complexity in the input sequence. Capitalizing on the advances in computer vision, the field of medical imaging has heralded a new epoch with Mamba models. Intending to help researchers navigate the surge, this survey seeks to offer an encyclopedic review of Mamba models in medical imaging. Specifically, we start with a comprehensive theoretical review forming the basis of SSMs, including Mamba architecture and its alternatives for sequence modeling paradigms in this context. Next, we offer a structured classification of Mamba models in the medical field and introduce a diverse categorization scheme based on their application, imaging modalities, and targeted organs. Consequently, we explore various applications and their practical use cases in medicine, such as image segmentation, reconstruction, registration, classification, language processing, multi-modal understanding, and other medically related challenges. Finally, we summarize key challenges, discuss different future research directions of the SSMs in the medical domain, and prop

关键词： Medical applications

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Tabular Q-learning Based Reinforcement Learning Agent for Autonomous Vehicle Drift Initiation and Stabilization

引用

IFAC-PapersOnLine 2023年第2期56卷 4896-4903页

作者： Szilárd H. Tóth Ádám Bárdos Zsolt J. Viharos Department of Automotive Technologies Budapest University of Technology and Economics Budapest Hungary Research Laboratory on Engineering and Management Intelligence Intelligent Processes Research Group Institute of Computer Science and Control Centre of Excellence in Production Informatics and Control Eötvös Loránd Research Network (ELKH) Centre of Excellence of the Hungarian Academy of Sciences (MTA) Budapest Hungary Department of Management and Business Law Faculty of Economics John von Neumann University Kecskemét Hungary

This paper aims to report on novel research results about developing a reinforcement learning agent for steady-state vehicle drift motion control. Based on the previous results of this research, the primary goal was to eliminate the problems causing learning instability experienced with the Soft Actor-Critic (SAC) algorithm applying Tabular Q-learning in this work. Trained in a MATLAB/Simulink-based simulation environment, the resulting agent succeeded in this task while being able to smoothly operate the vehicle to achieve and retain the desired target drift state, regardless of the discreet nature of the algorithm used for solving an inherently continuous task.

关键词： Motion Control Autonomous Vehicles Vehicle Dynamics Control Reinforcement Learning Vehicle Drifting Handling Limits

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Trans-DF: A Transfer Learning-based end-to-end Deepfake Detector 5

Trans-DF: A Transfer Learning-based end-to-end Deepfake Dete...

引用

5th IEEE International Conference on Computing Communication and Automation, ICCCA 2020

作者： Patel, Mohil Gupta, Aaryan Tanwar, Sudeep Obaidat, M.S. Institute of Technology Nirma University Department of Computer Science and Engineering Gujarat India University of Sharjah UAE King Abdullah II School of It University of Jordan College of Computing and Informatics Jordan

ISBN: (数字)9781728163246

ISBN: (纸本)9781728163246

With the advent of information and communication technologies, there have been breakthrough developments in the field of Artificial Intelligence (AI). Moreover, increasing computation power and decreasing processing times, new applications are being developed at great speeds. One such application is Deepfakes, which tackles the increased manipulated and forged media content. But these fake images and videos hamper the security and privacy of individuals and can have large-scale religious, communal, or political implications that may prove to be catastrophic for a nation. The face swapped content at times can be identified by human observation, but with the use of Generative adversarial networks (GANs), such forged content can be developed with is hard to be identified even by humans. Hence, detecting such videos and images is a challenging task for researchers. Motivated from these gaps, in this paper, we propose a pipeline for detecting and extracting human faces from videos, process them to extract features from them, and then classify them as real or fake. The results of the proposed model achieved an accuracy of 90.2% for classifying fake images from real ones. © 2020 IEEE.

关键词： Generative adversarial networks

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A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements

arXiv

引用

arXiv 2022年

作者： Zhang, Jiawei Li, Chen Rahaman, Md Mamunur Yao, Yudong Ma, Pingli Zhang, Jinghua Zhao, Xin Jiang, Tao Grzegorzek, Marcin Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States School of Resources and Civil Engineering Northeastern University Shenyang110004 China School of Control Engineering Chengdu University of Information Technology Chengdu610225 China Institute of Medical Informatics University of Luebeck Luebeck23538 Germany School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia

With the acceleration of urbanization and living standards, microorganisms play an increasingly important role in industrial production, bio-technique, and food safety testing. Microorganism biovolume measurements are one of the essential parts of microbial analysis. However, traditional manual measurement methods are time-consuming and challenging to measure the characteristics precisely. With the development of digital image processing techniques, the characteristics of the microbial population can be detected and quantified. The applications of the microorganism biovolume measurement method have developed since the 1980s. More than 62 articles are reviewed in this study, and the articles are grouped by digital image analysis methods with time. This study has high research significance and application value, which can be referred to as microbial researchers to comprehensively understand microorganism biovolume measurements using digital image analysis methods and potential applications. © 2022, CC0.

关键词： Microorganisms

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Critical subgraphs of Schrijver graphs for the fractional chromatic number

arXiv

引用

arXiv 2022年

作者： Gujgiczer, Anna Simonyi, Gábor Department of Computer Science and Information Theory Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics MTA-BME Lendület Arithmetic Combinatorics Research Group ELKH Budapest Hungary Alfréd Rényi Institute of Mathematics Budapest Hungary

Schrijver graphs are vertex-color-critical subgraphs of Kneser graphs having the same chromatic number. They also share the value of their fractional chromatic number but Schrijver graphs are not critical for that. Here we present an induced subgraph of every Schrijver graph that is vertex-critical with respect to the fractional chromatic number. These subgraphs turn out to be isomorphic with certain circular complete graphs. We also characterize the critical edges within this *** Codes Primary: 05C15, Secondary: 05C60, 05C72 Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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Dermoscopic Image Classification Using Attention Mechanism and Ensemble Learning Approaches

Dermoscopic Image Classification Using Attention Mechanism a...

引用

IEEE International Conference on Big Data

作者： Shanchuan Huang Hongwei Lei Liuhan Jin Jinzhu Yang Tao Jiang Yudong Yao Marcin Grzegorzek Chen Li Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang Liaoning China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China Department of Electrical and Computer Engineering Stevens Institute of Technology USA Institute of Medical Informatics University of Luebeck Luebeck Germany

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings.

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