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Deep neural network architecture search using network morphism

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Deep neural network architecture search using network morphi...

International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Arkadiusz Kwasigroch Michal Grochowski Mateusz Mikolajczyk Department of Electrical Engineering Control Systems and Informatics Gdansk University of Technology Poland

The paper presents the results of the research on neural architecture search (NAS) algorithm. We utilized the hill climbing algorithm to search for well-performing structures of deep convolutional neural network. Moreover, we used the function preserving transformations which enabled the effective operation of the algorithm in a short period of time. The network obtained with the advantage of NAS was validated on skin lesion classification problem. We compared the parameters and performance of the automatically generated neural structure with the architectures selected manually, reported by the authors in previous papers. The obtained structure achieved comparable results to hand-designed networks, but with much fewer parameters then manually crafted architectures.

关键词： Computer architecture Classification algorithms Task analysis Lesions Training Cancer Neural networks

Fast all-optical random number generator

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arXiv 2022年

作者： Zhao, Jiamin Li, Pu Zhang, Xing Gao, Zhensen Jia, Zhiwei Bogris, Adonis Alan Shore, K. Wang, Yuncai Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China Guangdong Provincial Key Laboratory of Photonics Information Technology Guangzhou510006 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai200444 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Department of Informatics and Computer Engineering University of West Attica Athens12243 Greece School of Electronic Engineering Bangor University WalesLL57 1UT United Kingdom

We propose a simple and all-optical method for fast random number generation based on the laser mode hopping. Through periodically restarting a two-mode laser operating in the bistable state, a random number stream can be generated due to the spontaneous emission noise. To validate the feasibility of this method, we perform a theoretical simulation using the common vertical-cavity surface-emitting laser (VCSEL) with two polarization modes. Numerical results demonstrate that fast 2.5 Gb/s random number streams can be continuously obtained with verified randomness. This scheme provide a fully monolithic solution for random number generator, due to its simple and all-optical structure. Copyright © 2022, The Authors. All rights reserved.

关键词： Random number generation

Non-contact monitoring of heart rate variability using medical radar for the evaluation of dynamic changes in autonomic nervous activity during a head-up tilt test

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Journal of Medical engineering and Technology 2019年第7期43卷 411-417页

作者： Sun, Guanghao Tanaka, Yosuke Kiyono, Ken Hashimoto, Kenichi Takase, Bonpei Liu, He Kirimoto, Tetsuo Matsui, Takemi Graduate School of Informatics and Engineering The University of Electro-Communications ChofuTokyo Japan Graduate School of System Design Tokyo Metropolitan University HinoTokyo Japan Division of Bioengineering Graduate School of Engineering Science Osaka University ToyonakaOsaka Japan Department of Intensive Care Medicine National Defense Medical College TokorozawaSaitama Japan Department of Measurement and Control Technology and Communication Engineering The Harbin University of Science and Technology Harbin China

Electrocardiography (ECG) is a mandatory standard for monitoring electrical activity of the heart in many clinical settings such as intensive care and emergency units. However, in situations wherein the skin is damaged, such as acute burn injuries, it is impossible to efficiently attach electrodes to the skin. In this study, we developed a non-contact cardiac monitoring system using a 24-GHz medical radar for directly measuring the beat-to-beat heart mechanical activity at a distance from a subject. The heart rate variability (HRV) was analysed using an autoregressive model (AR) from the measured beat-to-beat intervals during a head-up tilt test. To investigate the feasibility of the proposed system, we compared medical radar and ECG recording by using Lin’s correlation coefficient and Bland–Altman analysis, which showed a negligible mean difference from the substantial agreement of Lin’s correlation coefficient of 0.9 between the radar and ECG. The non-contact radar clearly monitored dynamic changes in HRV indices induced by the head-up tilt test. This type of non-contact HRV-sensing technique as an alternative approach has significant potential for advancing personal healthcare in both clinical and out-of-hospital settings. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Electrocardiography

Symbolic Regression Driven by Training Data and Prior Knowledge

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arXiv 2020年

作者： Kubalík, Jiří Derner, Erik Babuška, Robert Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague16000 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Prague16000 Czech Republic Cognitive Robotics Delft University of Technology Delft2628 CD Netherlands

In symbolic regression, the search for analytic models is typically driven purely by the prediction error observed on the training data samples. However, when the data samples do not sufficiently cover the input space, the prediction error does not provide sufficient guidance toward desired models. Standard symbolic regression techniques then yield models that are partially incorrect, for instance, in terms of their steady-state characteristics or local behavior. If these properties were considered already during the search process, more accurate and relevant models could be produced. We propose a multi-objective symbolic regression approach that is driven by both the training data and the prior knowledge of the properties the desired model should manifest. The properties given in the form of formal constraints are internally represented by a set of discrete data samples on which candidate models are exactly checked. The proposed approach was experimentally evaluated on three test problems with results clearly demonstrating its capability to evolve realistic models that fit the training data well while complying with the prior knowledge of the desired model characteristics at the same time. It outperforms standard symbolic regression by several orders of magnitude in terms of the mean squared deviation from a reference model. Copyright © 2020, The Authors. All rights reserved.

关键词： Genetic programming

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

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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

STREAMLINE: AN AUTOMATED MACHINE LEARNING PIPELINE FOR BIOMEDICINE APPLIED TO EXAMINE THE UTILITY OF PHOTOGRAPHY-BASED PHENOTYPES FOR OSA PREDICTION ACROSS INTERNATIONAL SLEEP CENTERS

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arXiv 2023年

作者： Urbanowicz, Ryan J. Bandhey, Harsh Keenan, Brendan T. Maislin, Greg Hwang, Sy Mowery, Danielle L. Lynch, Shannon M. Mazzotti, Diego R. Han, Fang Li, Qing Yun Penzel, Thomas Tufik, Sergio Bittencourt, Lia Gislason, Thorarinn de Chazal, Philip Singh, Bhajan McArdle, Nigel Chen, Ning-Hung Pack, Allan Schwab, Richard J. Cistulli, Peter A. Magalang, Ulysses J. Department of Computational Biomedicine Cedars Sinai Medical Center Los AngelesCA United States Division of Sleep Medicine Department of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Biomedical Informatics University of Pennsylvania PhiladelphiaPA United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania PhiladelphiaPA United States Cancer Prevention and Control Program Fox Chase Cancer Center PhiladelphiaPA United States Division of Medical Informatics Department of Internal Medicine University of Kansas LawrenceKS United States Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of Kansas LawrenceKS United States Division of Sleep Medicine Peking University People’s Hospital Beijing China Department of Respiratory and Critical Care Medicine Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Interdisciplinary Center of Sleep Medicine Charite University Hospital Berlin Germany Department of Psychobiology Federal University of Sao Paulo Sao Paulo Brazil Department of Sleep Medicine Landspitali University Hospital Reykjavik Iceland University of Iceland Faculty of Medicine Reykjavik Iceland Charles Perkins Center Faculty of Engineering University of Sydney Sydney Australia Department of Pulmonary Physiology and Sleep Medicine Sir Charles Gairdner Hospital Nedlands Australia University of Western Australia Crawley Australia Department of Pulmonary and Critical Care Medicine Sleep Center Linkou Chang Gung Memorial Hospital Taoyuan Taiwan Charles Perkins Centre Faculty of Medicine and Health University of Sydney Royal North Shore Hospital Sydney Australia Division of Pulmonary Critical Care and Sleep Medicine Department of Medicine Ohio State University Wexner Medical Center ColumbusOH United States 700 N. San Vicente Blvd. Pacific Design Center Suite G541E Los AngelesCA90069 United States

Objective: While machine learning (ML) includes a valuable array of tools for analyzing biomedical data with multivariate and complex underlying associations, significant time and expertise is required to assemble effective, rigorous, comparable, reproducible, and unbiased pipelines. Automated ML (AutoML) tools seek to facilitate ML application by automating a subset of analysis pipeline elements. In this study we develop and validate a Simple, Transparent, End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to investigate the added utility of photography-based phenotypes for predicting obstructive sleep apnea (OSA);a common and underdiagnosed condition associated with a variety of health, economic, and safety consequences. Methods: STREAMLINE is designed to tackle biomedical binary classification tasks while (1) avoiding common mistakes, (2) accommodating complex associations and common data challenges, and (3) allowing scalability, reproducibility, and model interpretation. It automates the majority of established, generalizable, and reliably automatable aspects of an ML analysis pipeline while incorporating cutting edge algorithms and providing opportunities for human-in-the-loop customization. We present a broadly refactored and extended release of STREAMLINE, validating and benchmarking performance across simulated and real-world datasets. Then we applied STREAMLINE to evaluate the utility of demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and photography-based craniofacial (CF) and intraoral (IO) anatomy measures in predicting ‘any OSA’ or ‘moderate/severe OSA’ using 3,111 participants from Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Results: Benchmarking analyses validated the efficacy of STREAMLINE across data simulations with increasingly complex patterns of association including epistatic interactions and genetic heterogeneity. OSA analyses identified a significant increase in ROC-AUC when adding CF t

关键词： Machine learning

Muscle activity measurement using visible light and infrared

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IFAC-PapersOnLine 2019年第27期52卷 329-334页

作者： Mariusz Sikora Szczepan Paszkiel Faculty of Electrical Engineering Automatic Control and Informatics Department of Biomedical Engineering Opole University of Technology Proszkowska 76 45-271 Opole Poland

This article describes possibilities of using optical sensor MAX30105 as a tool for muscle activity detection and measurement. On the beginning, there is a preview to another works, where muscle activity was measured for different purposes and with a number of methods. Own measurement with the usage of visible light as well as near infrared was focused on upper limbs, especially on forearm and shoulder muscles. These data are compared with signals registered with gyroscope and one channel electromyography (EMG). Some measurements were done on several volunteers in order to confirm the effectiveness of this method.

关键词： EMG muscles electromyography measurements infrared

Lessening stress and anxiety-related behaviors by means of AI-driven drones for aromatherapy 6

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Lessening stress and anxiety-related behaviors by means of A...

6th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2019

作者： Capizzi, Giacomo Napoli, Christian Russo, Samuele Woźniak, Marcin Department of Electrical Electronics and Informatics Engineering University of Catania Viale Andrea Doria 6 Catania CT95125 Italy Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma RM00185 Italy University of Catania Piazza Università 2 Catania CT95124 Italy Specialization School in Psychotherapy C.T.R. Via Galermo 105 Catania CT95123 Italy Institute of Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

Stress and anxiety are part of the human mental process which is often unavoidably yield by circumstances and situations such as waiting for a flight at the airport gate, hanging around before an exam, or while in an hospital waiting room. In this work we devise a decision system for a robotic aroma diffusion device designed to lessen stress and anxiety-related behaviors. The robot is intended as designed for deployments in closed environments that resembles the aspect and structure of a waiting room with different chairs where people sit and wait. The robot can be remotely driven by means of an artificial intelligence based on Radial Basis Function Neural Networks classifiers. The latter is responsible to recognize when stress or anxiety levels are arising so that the diffusion of specific aromas could relax the bystanders. We make use of thermal images to infer the level of stress by means of an ad hoc feature extraction approach. The system is prone to future improvements such as the refinement of the classification process also by means of accurate psychometric studies that could be based on standardized tests or derivatives. 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： Feature extraction