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Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data

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arXiv 2024年

作者： Chen, Shan Gallifant, Jack Guevara, Marco Gao, Yanjun Afshar, Majid Miller, Timothy Dligach, Dmitriy Bitterman, Danielle S. Program Mass General Brigham Harvard Medical School BostonMA United States Department of Radiation Oncology Brigham and Women's Hospital Dana-Farber Cancer Institute BostonMA United States Computational Health Informatics Program Boston Children's Hospital Harvard Medical School BostonMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States Department of Critical Care Guy's & St Thomas' NHS Trust London United Kingdom Department of Medicine University of Wisconsin School of Medicine and Public Health MadisonWI United States Department of Computer Science Loyola University Chicago ChicagoIL United States

Brief communication 70 words: Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain. © 2024, CC BY-NC-SA.

关键词： Natural language processing systems

GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

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arXiv 2023年

作者： Dou, Haoran Bi, Ning Han, Luyi Huang, Yuhao Mann, Ritse Yang, Xin Ni, Dong ravikumar, Nishant Frangi, Alejandro F. Huang, Yunzhi University of Leeds Leeds United Kingdom Department of Radiology and Nuclear Medicine Radboud University Medical Centre Nijmegen Netherlands Department of Radiology Netherlands Cancer Institute Amsterdam Netherlands National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Health Science Center Shenzhen University China Lab Shenzhen University China Marshall Laboratory of Biomedical Engineering Shenzhen University China Shenzhen RayShape Medical Technology Co. Ltd China Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester United Kingdom Electrical Engineering and Cardiovascular Sciences Departments KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness. © 2023, CC BY.

关键词： Medical applications

Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided radiotherapy

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arXiv 2024年

作者： Moradi, Nikoo Ferreira, André Puladi, Behrus Kleesiek, Jens Fatemizadeh, Emad Luijten, Gijs Alves, Victor Egger, Jan Department of Electrical Engineering Sharif University of Technology Tehran Iran University Medicine Essen Girardetstraße 2 Essen45131 Germany Center Algoritmi / LASI University of Minho Braga4710-057 Portugal Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Aachen Germany Institute of Medical Informatics University Hospital RWTH Aachen Aachen Germany Essen Germany Partner Site Essen Essen Germany Department of Physics TU Dortmund University Dortmund Germany University Medicine Essen Essen Germany Graz University of Technology Inffeldgasse 16/II Graz8010 Austria

radiation therapy (RT) is essential in treating head and neck cancer (HNC), with magnetic resonance imaging(MRI)-guided RT offering superior soft tissue contrast and functional imaging. However, manual tumor segmentation is time-consuming and complex, and therfore remains a challenge. In this study, we present our solution as team TUMOR to the HNTS-MRG24 MICCAI Challenge which is focused on automated segmentation of primary gross tumor volumes (GTVp) and metastatic lymph node gross tumor volume (GTVn) in pre-RT and mid-RT MRI images. We utilized the HNTS-MRG2024 dataset, which consists of 150 MRI scans from patients diagnosed with HNC, including original and registered pre-RT and mid-RT T2-weighted images with corresponding segmentation masks for GTVp and GTVn. We employed two state-of-the-art models in deep learning, nnUNet and MedNeXt. For Task 1, we pretrained models on pre-RT registered and mid-RT images, followed by fine-tuning on original pre-RT images. For Task 2, we combined registered pre-RT images, registered pre-RT segmentation masks, and mid-RT data as a multi-channel input for training. Our solution for Task 1 achieved 1st place in the final test phase with an aggregated Dice Similarity Coefficient of 0.8254, and our solution for Task 2 ranked 8th with a score of 0.7005. The proposed solution is publicly available at Github Repository. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

Sustainable Wireless Networks via Reconfigurable Intelligent Surfaces (RISs): Overview of the ETSI ISG RIS

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arXiv 2024年

作者： Liu, Ruiqi Zheng, Shuang Wu, Qingqing Jiang, Yifan Zhang, Nan Liu, Yuanwei Di Renzo, Marco Alexandropoulos, George C. The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China Wireless and Computing Research Institute ZTE Corporation Shenzhen China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 Rue Joliot-Curie Gif-sur-Yvette91192 France The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece

Reconfigurable Intelligent Surfaces (RISs) are a novel form of ultra-low power devices that are capable to increase the communication data rates as well as the cell coverage in a cost- and energy-efficient way. This is attributed to their programmable operation that enables them to dynamically manipulate the wireless propagation environment, a feature that has lately inspired numerous research investigations and applications. To pave the way to the formal standardization of RISs, the European Telecommunications Standards institute (ETSI) launched the Industry Specification Group (ISG) on the RIS technology in September 2021. This article provides a comprehensive overview of the status of the work conducted by the ETSI ISG RIS, covering typical deployment scenarios of reconfigurable metasurfaces, use cases and operating applications, requirements, emerging hardware architectures and operating modes, as well as the latest insights regarding future directions of RISs and the resulting smart wireless environments. © 2024, CC BY-NC-ND.

关键词： Standardization

Secrecy Analysis and Optimization of UAV-Assisted Communications With Hybrid SWIPT and Cooperative Jamming

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IEEE Journal on Miniaturization for Air and Space Systems

IEEE Journal on Miniaturization for Air and Space Systems 2025年

作者： Pandey, Gaurav K. Gurjar, Devendra S. Yadav, Suneel Solanki, Sourabh Gazda, Juraj Chatzinotas, Symeon National Institute of Technology Silchar Department of Electronics and Communication Engineering Assam Cachar788010 India Indian Institute of Information Technology Allahabad Department of Electronics and Communication Engineering Uttar Pradesh Prayagraj211015 India National Institute of Technology Department of Electronics and Communication Engineering Warangal India Technical University of Košice Department of Computer and Informatics Košice04200 Slovakia University of Luxembourg SnT Luxembourg City1855 Luxembourg

Unmanned aerial vehicles (UAVs) have emerged as a state-of-the-art solution for establishing communication in remote and obstructed areas. However, before UAVs can be integrated into existing communication infrastructure, it is essential to address the energy constraints and security concerns arising from their line-of-sight links. This paper focuses on a UAV-enabled communication system in which a UAV relay facilitates information transfer from the source to the destination nodes when the direct link is heavily shadowed or obstructed. A nearby terrestrial passive eavesdropper can intercept information transmitted through the source-to-UAV and UAV-to-destination links. To address this, we utilize destination-aided cooperative jamming. Additionally, we consider simultaneous wireless information and power transfer (SWIPT) at the UAV to provide the energy required for data transmission. In particular, the UAV utilizes a hybrid-SWIPT technique to harvest energy from the radio-frequency signals. For this setup, we derive accurate expressions of secrecy outage probability and system secrecy throughput (SST) over Beaulieu-Xie distributed channels. Using the SST expression, we formulate an SST maximization problem to jointly optimize the transmit powers, power allocation, SWIPT coefficients, and UAV’s three-dimensional position. The formulated problem is solved using the hybrid heuristic framework, combining continuous genetic and particle swarm optimization algorithms. Numerical results demonstrate the significant enhancement in information secrecy of the system with the proposed hybrid scheme and also provide valuable insights into the system’s behavior. © 2019 IEEE.

关键词： Drones

Query Minimization Under Stochastic Uncertainty 14th

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Query Minimization Under Stochastic Uncertainty

14th Latin American Symposium on Theoretical informatics, LATIN 2020

作者： Chaplick, Steven Halldórsson, Magnús M. de Lima, Murilo S. Tonoyan, Tigran Lehrstuhl für Informatik I Universität Würzburg Würzburg Germany Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands ICE-TCS Department of Computer Science Reykjavik University Reykjavik Iceland School of Informatics University of Leicester Leicester United Kingdom Computer Science Department Technion Institute of Technology Haifa Israel

ISBN: (纸本)9783030617912

We study problems with stochastic uncertainty data on intervals for which the precise value can be queried by paying a cost. The goal is to devise an adaptive decision tree to find a correct solution to the problem in consideration while minimizing the expected total query cost. We show that sorting in this scenario can be performed in polynomial time, while finding the data item with minimum value seems to be hard. This contradicts intuition, since the minimum problem is easier both in the online setting with adversarial inputs and in the offline verification setting. However, the stochastic assumption can be leveraged to beat both deterministic and randomized approximation lower bounds for the online setting. Although some literature has been devoted to minimizing query/probing costs when solving uncertainty problems with stochastic input, none of them have considered the setting we describe. Our approach is closer to the study of query-competitive algorithms, and it gives a better perspective on the impact of the stochastic assumption. © 2020, Springer Nature Switzerland AG.

关键词： Sorting

Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications

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arXiv 2024年

作者： Nguyen, Cong T. Saputra, Yuris Mulya Van Huynh, Nguyen Nguyen, Tan N. Hoang, Dinh Thai Nguyen, Diep N. Pham, Van-Quan Voznak, Miroslav Chatzinotas, Symeon Tran, Dinh-Hieu Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh City70000 Viet Nam Faculty of Information Technology Duy Tan University Da Nang50000 Viet Nam Internet Engineering Technology Department of Electrical Engineering and Informatics Vocational College Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Nokia Bell Labs Murray Hill NJ07974 United States Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava708 00 Czech Republic University of Luxembourg Esch-sur-Alzette4365 Luxembourg Nokia France

Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open radio Access Network (OraN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and OraN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how OraN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era. © 2024, CC BY.

关键词： radio access networks

Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

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arXiv 2022年

作者： Horváth, Dániel Bocsi, Kristóf Erdős, Gábor Istenes, Zoltán Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Eötvös Loránd Research Network Budapest Hungary CoLocation Center for Academic and Industrial Cooperation Eötvös Loránd University Budapest Hungary Department of Manufacturing Science and Engineering Budapest University of Technology and Economics Budapest Hungary

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data. © 2022, CC BY-NC-ND.

关键词： Cyber Physical System

NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers

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arXiv 2023年

作者： Wei, Xinru Dong, Shuai Su, Zhao Tang, Lili Zhao, Pengfei Pan, Chunyu Wang, Fei Tang, Yanqing Zhang, Weixiong Zhang, Xizhe Early Intervention Unit Department of Psychiatry The Affiliated Brain Hospital of Nanjing Medical University Jiangsu Nanjing210029 China School of Biomedical Engineering and Informatics Nanjing Medical University Jiangsu Nanjing210001 China School of Computer Science and Engineering Northeastern University Shenyang China Department of Health Technology and Informatics Department of Computing The Hong Kong Polytechnic University Hong Kong Functional Brain Imaging Institute of Nanjing Medical University Nanjing China Department of Psychiatry The First Affiliated Hospital of China Medical University Shenyang China Brain Function Research Section The First Affiliated Hospital of China Medical University Shenyang China Department of Gerontology The First Affiliated Hospital of China Medical University Shenyang China

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST’s capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Neuroimaging