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BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery

computers in Biology and Medicine 2023年 167卷 107602-107602页

作者： Plotka, Szymon Grzeszczyk, Michal K. Brawura-Biskupski-Samaha, Robert Gutaj, Pawel Lipa, Michal Trzciński, Tomasz Išgum, Ivana Sánchez, Clara I. Sitek, Arkadiusz Sano Centre for Computational Medicine Cracow Poland Informatics Institute University of Amsterdam Amsterdam Netherlands Department of Biomedical Engineering and Physics Amsterdam University Medical Center Amsterdam Netherlands Second Department of Obstetrics and Gynecology The Medical Centre of Postgraduate Education Warsaw Poland Department of Reproduction Poznan University of Medical Sciences Poznan Poznan Poland First Department of Obstetrics and Gynecology Medical University of Warsaw Warsaw Poland Institute of Computer Science Warsaw University of Technology Warsaw Poland Department of Radiology and Nuclear Medicine Amsterdam University Medical Center location University of Amsterdam Amsterdam Netherlands Center for Advanced Medical Computing and Simulation Massachusetts General Hospital Harvard Medical School BostonMA United States

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise. © 2023 The Author(s)

关键词： Forecasting

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Thoughts on neurophysiological signal analysis and classification

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Brain science Advances 2020年第3期6卷 210-223页

作者： Junhua Li Laboratory for Brain–Bionic Intelligence and Computational Neuroscience Wuyi UniversityJiangmen 529020GuangdongChina Centre for Multidisciplinary Convergence Computing School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’an 710072ShaanxiChina School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

Neurophysiological signals are crucial intermediaries,through which brain activity can be quantitatively measured and brain mechanisms are able to be *** particular,non-invasive neurophysiological signals,such as electroencephalogram(EEG)and functional magnetic resonance imaging(f MRI),are welcomed and frequently utilised in various studies since these signals can be non-invasively recorded without harming the human brain while they convey abundant information pertaining to brain *** recorded neurophysiological signals are analysed to mine meaningful information for the understanding of brain mechanisms or are classified to distinguish different patterns(e.g.,different cognitive states,brain diseases versus healthy controls).To date,remarkable progress has been made in both the analysis and classification of neurophysiological signals,but scholars are not feeling *** effort ought to be paid to advance the research of analysis and classification based on neurophysiological *** this paper,I express my thoughts regarding promising future directions in neurophysiological signal analysis and classification based on current developments and accomplishments.I will elucidate the thoughts after brief summaries of relevant backgrounds,accomplishments,and *** to my personal selection and preference,I mainly focus on brain connectivity,multidimensional array(tensor),multi-modality,multiple task classification,deep learning,big data,and naturalistic ***,my thoughts could give a little help to inspire new ideas and contribute to the research of the analysis and classification of neurophysiological signals in some way.

关键词： electroencephalogram(EEG) magnetic resonance imaging(MRI) tensor brain connectivity multiple tasks multi-modality classification big data deep learning naturalistic experiment

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Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers

arXiv

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arXiv 2021年

作者： Zhu, Tianyu Hiller, Markus Ehsanpour, Mahsa Ma, Rongkai Drummond, Tom Reid, Ian Rezatofighi, Hamid The Department of Electrical and Computer Systems Engineering Monash University Australia The School of Computing and Information Systems The University of Melbourne Australia The Australian Institute for Machine Learning The University of Adelaide Australia The Department of Data Science and AI Monash University Australia The Australian Centre for Robotic Vision Australia

Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field. Most existing approaches are not able to properly handle multi-object tracking challenges such as occlusion, in part because they ignore long-term temporal information. To address these shortcomings, we present MO3TR: a truly end-to-end Transformer-based online multi-object tracking (MOT) framework that learns to handle occlusions, track initiation and termination without the need for an explicit data association module or any heuristics. MO3TR encodes object interactions into long-term temporal embeddings using a combination of spatial and temporal Transformers, and recursively uses the information jointly with the input data to estimate the states of all tracked objects over time. The spatial attention mechanism enables our framework to learn implicit representations between all the objects and the objects to the measurements, while the temporal attention mechanism focuses on specific parts of past information, allowing our approach to resolve occlusions over multiple frames. Our experiments demonstrate the potential of this new approach, achieving results on par with or better than the current state-of-the-art on multiple MOT metrics for several popular multi-object tracking benchmarks. © 2021, CC BY.

关键词： Tracking (position)

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Kurdish Handwritten Character Recognition using Deep Learning Techniques

arXiv

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

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Syntax-Informed Interactive Neural Machine Translation

Syntax-Informed Interactive Neural Machine Translation

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International Joint Conference on Neural Networks (IJCNN)

作者： Kamal Kumar Gupta Rejwanul Haque Asif Ekbal Pushpak Bhattacharyya Andy Way Department of Computer Science and Engineering Indian Institute of Technology Patna Patna India ADAPT Centre School of Computing Dublin City University Dublin Ireland

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In interactive machine translation (MT), human translators correct errors in automatic translations in collaboration with the MT systems, and this is an effective way to improve productivity gain in translation. Phrase-based statistical MT (PB-SMT) has been the mainstream approach to MT for the past 30 years, both in academia and industry. Neural MT (NMT), an end-to-end learning approach to MT, represents the current state-of-the-art in MT research. The recent studies on interactive MT have indicated that NMT can significantly outperform PB-SMT. In this work, first we investigate the possibility of integrating lexical syntactic descriptions in the form of supertags into the state-of-the-art NMT model, Transformer. Then, we explore whether integration of supertags into Transformer could indeed reduce human efforts in translation in an interactive-predictive platform. From our investigation we found that our syntax-aware interactive NMT (INMT) framework significantly reduces simulated human efforts in the French-to-English and Hindi- to-English translation tasks, achieving a 2.65 point absolute corresponding to 5.65% relative improvement and a 6.55 point absolute corresponding to 19.1% relative improvement, respectively, in terms of word prediction accuracy (WPA) over the respective baselines.

关键词： Syntactics Predictive models Task analysis Protocols Games Grammar Context modeling

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Macroscopic Tunneling Probe of Moiré Spin Textures in Twisted CrI3

arXiv

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arXiv 2024年

作者： Yang, Bowen Patel, Tarun Cheng, Meixin Pichugin, Kostyantyn Tian, Lin Sherlekar, Nachiket Yan, Shaohua Fu, Yang Tian, Shangjie Lei, Hechang Reimer, Michael E. Okamoto, Junichi Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada Department of Physics Beijing Key Laboratory of Optoelectronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China School of Materials Science and Engineering Anhui University Hefei230601 China Institute of Physics University of Freiburg Hermann-Herder-Str. 3 Freiburg79104 Germany EUCOR Centre for Quantum Science and Quantum Computing University of Freiburg Hermann-Herder-Str. 3 Freiburg79104 Germany

Various noncollinear spin textures and magnetic phases have been predicted in twisted two-dimensional CrI3 due to competing ferromagnetic (FM) and antiferromagnetic (AFM) interlayer exchange from moiré stacking — with potential spintronic applications even when the underlying material possesses a negligible Dzyaloshinskii–Moriya or dipole–dipole interaction. Recent measurements have shown evidence of coexisting FM and AFM layer order in small-twist-angle CrI3 bilayers and double bilayers. Yet, the nature of the magnetic textures remains unresolved and possibilities for their manipulation and electrical readout are unexplored. Here, we use tunneling magnetoresistance to investigate the collective spin states of twisted double-bilayer CrI3 under both out-of-plane and in-plane magnetic fields together with detailed micromagnetic simulations of domain dynamics based on magnetic circular dichroism. Our results capture hysteretic and anisotropic field evolutions of the magnetic states and we further uncover two distinct non-volatile spin textures (out-of-plane and in-plane domains) at ≈1° twist angle, with a different global tunneling resistance that can be switched by magnetic field. © 2024, CC BY-NC-ND.

关键词： Chromium compounds

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Investigating Polarization characteristics of GRB 200503A and GRB 201009A

arXiv

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arXiv 2024年

作者： Saraogi, Divita Bala, Suman Joshi, Jitendra Iyyani, Shabnam Bhalerao, Varun Aditya, J. Venkata Svinkin, D.S. Frederiks, D.D. Lysenko, A.L. Ridnaia, A.V. Kozyrev, A.S. Golovin, D.V. Mitrofanov, I.G. Litvak, M.L. Sanin, A.B. Chattopadyay, Tanmoy Gupta, Soumya Waratkar, Gaurav Bhattacharya, Dipankar Vadawale, Santosh Dewangan, Gulab Department of Physics Indian Institute of Technology Bombay Maharashtra Mumbai400076 India Science and Technology Institute Universities Space Research Association HuntsvilleAL35805 United States Inter University Centre for Astronomy and Astrophysics Maharashtra Pune411007 India School of Physics Indian Institute of Science Education and Research Thiruvananthapuram695551 India Centre of High Performance Computing Indian Institute of Science Education and Research Thiruvananthapuram695551 India Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India Ioffe Institute Polytekhnicheskaya 26 St. Petersburg194021 Russia 84/32 Profsoyuznaya Moscow117997 Russia Kavli Institute of Particle Astrophysics and Cosmology 452 Lomita Mall StanfordCA94305 United States Astrophysical Science Division Homi Bhabha National Institute Anushakti Nagar Maharashtra Mumbai400094 India Department of Physics Ashoka University Haryana Sonepat131029 India Physical Research Laboratory Gujarat Ahmedabad380009 India

We present results of a comprehensive analysis of the polarization characteristics of GRB 200503A and GRB 201009A observed with the Cadmium Zinc Telluride Imager (CZTI) on board AstroSat. Despite these GRBs being reasonably bright, they were missed by several spacecraft and had thus far not been localized well, hindering polarization analysis. We present positions of these bursts obtained from the Inter-Planetary Network (IPN) and the newly developed CZTI localization pipeline. We then undertook polarization analyses using the standard CZTI pipeline. We cannot constrain the polarization properties for GRB 200503A, but find that GRB 201009A has a high degree of polarization. © 2024, CC BY.

关键词： Gamma rays

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A Bayesian Neural Network-Based Method For The Extraction Of A Metabolite Corrected Arterial Input Function From Dynamic [11C]PBR28 PET

A Bayesian Neural Network-Based Method For The Extraction Of...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： A. C. Whitehead L. Brusaferri L. Maccioni M. Ferrante M. Inglese Z. Alshelh M. Veronese N. Toschi J. Gilman K. Thielemans M. L. Loggia Institute of Nuclear Medicine University College London London United Kingdom Department of Computer Science University College London London United Kingdom Harvard Medical School Athinoula A. Martinos Center for Biomedical Imaging Boston Massachusetts United States of America Department of Information Engineering University of Padua Padua Italy Department of Biomedicine and Prevention University of Rome Tor Vergata Rome Italy Neuroimaging Department King’s College London London United Kingdom Centre for Medical Image Computing University College London London United Kingdom

In PET, arterial sampling and metabolite correction are prerequisites for the gold-standard measurement of values like the volume of distribution (V T ), often necessary for the full quantification of radioligand binding. However, the invasiveness and technical demands of these procedures limit their application in both research and clinical PET studies. Machine learning approaches have been explored to predict V T from PET images, but their integration in clinical routine is limited by their lack of transparency or thorough evaluation. Here we propose a Bayesian Neural Network to estimate the arterial input function (AIF), while also outputting its prediction uncertainty, 1) directly from the entire dynamic PET images (NN-AEIF), 2) from an image-derived input function (IDIF)(NN-IDIF) and, as a sensitivity measure, 3) from the un-corrected plasma curve (NN-AIF). All methods, applied on [ 11 C]PBR28 PET data, were compared to the metabolite-corrected AIF in terms of V T , and the prediction uncertainty was assessed in terms of normalised coefficient of variance (nCV). Overall, both NN-AEIF and NN-AIF were able to accurately predict V T , outperforming the other methods, with NN-AEIF showing the lowest nCV.

关键词：

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The computer Vision-based Tolerancing Callout Detection Model 16

The Computer Vision-based Tolerancing Callout Detection Mode...

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16th CIRP Conference on computer Aided Tolerancing, CIRP CAT 2020

作者： Deng, Xin Li, Tukun Xu, Yuanping Cao, Yanlong Kong, Chao Zhang, Errui School of Software Engineering Chengdu University of Information Technology Chengdu China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom Shandong Industrial Technology Research Institute Zhejiang University Shandong277500 China College of Engineering and Computer Science Australia National University CanberraACT2600 Australia

Tolerancing symbols play an important role in mechanical product drawings, and they directly determine the functions, mating properties, interchangeability and working life of geometrical products. A symbolic tolerancing callout containing a set of symbols represents a set of pre-ordered operations with attributes. It includes an amount of knowledge from the drawing and standard documents, which is often reconstructed manually by engineers. Thus, at the same time, a symbolic tolerancing callout makes it difficult for the end-user to understand and interpret these callouts manually. To this end, this study puts forward a tolerancing callout detection model via the use of off-the-shelf costumer-grade cameras on current mobile devices for extracting and recognizing tolerancing callout blocks and symbols in them intelligently. This model has four core components: image preprocessing, callout location and extraction, symbol and character segmentation, and deep learning-based symbol recognition. The image preprocessing component is developed to remove the interferences on the target technical drawings through the corresponding morphological methods. This study proposes a novel solution on callout block locations and extractions in callout intensive scenarios since the callout locations and extractions can directly affect the accuracy of symbol and character recognitions. Then, Huff Transform and improved projection methods have been devised to symbol and character segmentations. Finally, this study constructed a convolutional neural network (CNN) to train a symbol recognition model. The experimental results show that the proposed model gains applicability on intelligent callout extractions and the corresponding symbol recognitions. © 2020 The Authors. Published by Elsevier B.V.

关键词： Extraction

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Data Anonymous Authentication for BIoMT with Proxy Group Signature

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Journal of Cyber Security 2021年第4期3卷 207-216页

作者： Chaoyang Li Yalan Wang Gang Xu Xiubo Chen Xiangjun Xin Jian Li College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450001China Department of Computer Science University of SurreyGuildfordSurreyGU27XHUK School of Computing Science and Technology North China University of TechnologyBeijing100144China Information Security Centre State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing100876China

Along with the increase of wearable medical device,the privacy leakage problem in the process of transmission between these edge medical *** blockchain-enabled Internet of Medical Things(BIoMT)has been developed to reform traditional centralized medical system in recent *** paper first introduces a data anonymous authentication model to protect user privacy and medical data in ***,a proxy group signature(PGS)scheme has been proposed based on lattice *** scheme can well satisfy the anonymous authentication demand for the proposed model,and provide anti-quantum attack security for BIoMT in the future general quantum computer ***,the security analysis shows this PGS scheme is secure against the dynamical-almost-full anonymous and *** efficiency comparison shows the proposed model and PGS scheme is more efficient and practical.

关键词： Blockchain-enabled Internet of Medical Things anonymous authentication proxy group signature medical data

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