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ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions

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Computers in Biology and Medicine 2024年 171卷 108217-108217页

作者： Tang, Dechao Li, Chen Du, Tianmin Jiang, Huiyan Ma, Deguo Ma, Zhiyu Grzegorzek, Marcin Jiang, Tao Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University China Software College Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Poland Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology China Department of Radiology Shengjing Hospital China Medical University Shenyang China

Background: Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. Methods: In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. Results: This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section's maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. Conclusion: As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted d

关键词： Object detection

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Empowering Personalized Health Data Queries with Knowledge Graph and GPT-Enhanced Voice Assistant

Empowering Personalized Health Data Queries with Knowledge G...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xin Wang Wordh Ul Hasan Juan Li Rasha Hendawi Yang Du Bo Xie Cui Tao Department of Computer Science North Dakota State University Fargo USA Department of Computer Systems and Software Engineering Valley City State University Valley City USA School of Nursing The University of Texas at Austin Austin USA School of Biomedical Informatics University of Texas Health Science Center at Houston Houston USA

The proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inconvenient, particularly for older individuals unfamiliar with technology. While ChatGPT offers a conversational interface, it lacks domain-specific knowledge, including personalized health information. To address this limitation, we present a novel approach that combines a knowledge graph and GPT to enable personalized health queries. Our solution utilizes a personal knowledge graph as a comprehensive knowledge source and fine-tunes GPT to provide accurate responses. We have implemented a voice assistant mobile app incorporating this knowledge graph-assisted GPT model and conducted initial feasibility testing.

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The ethical implications of shared medical decision making without providing adequate computational support to the care provider and to the patient

arXiv

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arXiv 2021年

作者： Shahar, Yuval Medical Informatics Research Center Department of Software and Information Systems Engineering Ben Gurion University Beer Sheva84105 Israel

There is a clear need to involve patients in medical decisions. However, cognitive psychological research has highlighted the cognitive limitations of humans with respect to (a) probabilistic assessment of the patient's state and of potential outcomes of various decisions, (b) elicitation of the patient's utility function, and (c) integration of the probabilistic knowledge and of patient preferences to determine the optimal strategy. Therefore, without adequate computational support, current shared-decision models have severe ethical deficiencies. An "informed-consent" model unfairly transfers the responsibility to a patient who does not have the necessary knowledge, nor the integration capability. A "paternalistic" model endows with exaggerated power a physician who might not be aware of the patient's preferences, is prone to multiple cognitive biases, and whose computational integration capability is bounded. Recent progress in Artificial Intelligence suggests adding a third agent: a computer, in all deliberative medical decisions: Non-emergency medical decisions in which (a) more than one alternative exists, (b) the patient's preferences can be elicited, (c) the therapeutic alternatives might be influenced by these preferences, (d) medical knowledge exists regarding the likelihood of the decisions' outcomes, and (e) there is sufficient decision time. Ethical physicians should exploit computational decision-support technologies, neither making the decisions solely on their own, nor shirking their duty and shifting the responsibility to patients in the name of "informed consent." The resulting three-way (patient, care provider, computer) human-machine model that we suggest emphasizes the patient's preferences, the physician's knowledge, and the computational integration of both aspects, does not diminish the physician's role, but rather brings out the best in human and machine. © 2021, CC BY-NC-ND.

关键词： Decision making

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Implementation and evaluation of a multivariate abstraction-based, interval-based dynamic time-warping method as a similarity measure for longitudinal medical records

arXiv

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arXiv 2021年

作者： Lion, Matan Shahar, Yuval Medical Informatics Research Center Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

Objectives: A common prerequisite for tasks such as classification, prediction, clustering and retrieval of longitudinal medical records is a clinically meaningful similarity measure that considers both [multiple] variable (concept) values and their time. Currently, most similarity measures focus on raw, time-stamped data as these are stored in a medical record. However, clinicians think in terms of clinically meaningful temporal abstractions, such as "decreasing renal functions", enabling them to ignore minor time and value variations and focus on similarities among the clinical trajectories of different patients. Our objective was to define an abstraction- and interval-based methodology for matching longitudinal, multivariate medical records, and rigorously assess its value, versus the option of using just the raw, time-stamped data. Methods: We have developed a new methodology for determination of the relative distance between a pair of longitudinal records, by extending the known dynamic time warping (DTW) method into an interval-based dynamic time warping (iDTW) methodology. The iDTW methodology includes (A): A three-steps interval-based representation (iRep) method: [1] abstracting the raw, time-stamped data of the longitudinal records into clinically meaningful interval-based abstractions, using a domain-specific knowledge base, [2] scoping the period of comparison of the records, [3] creating from the intervals a symbolic time series, by partitioning them into a predetermined temporal granularity;(B) An interval-based matching (iMatch) method to match each relevant pair of multivariate longitudinal records, each represented as multiple series of short symbolic intervals in the determined temporal granularity, using a modified DTW version. Evaluation: Three classification or prediction tasks were defined: (1) classifying 161 records of oncology patients as having had autologous versus allogenic bone-marrow transplantation;(2) classifying the longitudinal reco

关键词： Knowledge representation

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Edge AI-Based Dynamic Treatment Strategy Generation Model for Consumer Healthcare Technology

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IEEE Transactions on Consumer Electronics 2024年

作者： Byeon, Haewon Alsaadi, Mahmood Quraishi, Aadam Shabaz, Mohammad Ahanger, Tariq Ahamed Keshta, Ismail Saidova, Feruza Soni, Mukesh Karumuri, Srinivasa Rao Inje University Department of AI and Software Gimhae50834 Korea Republic of Al-Maarif University College Department of computer science Al Anbar31001 Iraq Intervention Treatment Institute M.D. Research HoustonTX United States Model Institute of Engineering and Technology J and K Jammu India CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia AlMaarefa University College of Applied Sciences Computer Science and Information Systems Department Riyadh Saudi Arabia Tashkent State University of Economics Department of Foreign languages Tashkent Uzbekistan Lovely Professional University Division of Research and Development Phagwara India VLSI-Microelectronics Lab Department of Electronics and Communication Engineering Andhra Pradesh Vaddeswaram Guntur522302 India

Edge AI-based reinforcement learning is less reliant on mathematical models and relies on experience to help with the design and optimization of Consumer Technology models, making it ideal for learning dynamic treatment techniques. However, the current research still has the following problems: 1. The optimality of the learned strategy is considered without taking risks into account, leading to strategies with certain risks. 2. The issue of distribution shift is ignored, resulting in learned strategies that are completely different from those of doctors. 3. The patient's historical observational data and treatments are ignored, making it difficult to accurately assess the patient's state, which in turn leads to the inability to learn the optimal strategy. Based on this, we propose the DOSAC-DTR dynamic treatment strategy model, which integrates Dead-ends and offline monitoring into the Actor-Critic framework. First, we consider the risk of treatment actions recommended by the learned strategy and incorporate the concept of Dead-ends into the Actor-Critic framework. Second, to mitigate the distribution shift problem, we integrate physician monitoring into the Actor-Critic framework. This approach achieves better performance, resulting in lower estimated mortality rates and higher Jaccard index scores. © 1975-2011 IEEE.

关键词： Reinforcement learning

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Fundus Image Translation with Scale-Aware Registration and Gradient-Guided GAN

SSRN

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SSRN 2024年

作者： Kim, JuChan Le, Duc-Tai Bum, Junghyun Son, Chang-Hwan Lee, Eun Jung Han, Jong Chul Choo, Hyunseung Department of Artificial Intelligence Systems Engineering Sungkyunkwan University Korea Republic of College of Computing and Informatics Sungkyunkwan University Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Korea Republic of Software Science and Engineering Major Kunsan National University Korea Republic of Department of Ophthalmology Samsung Medical Center Korea Republic of School of Medicine Sungkyunkwan University Korea Republic of Department of Medical Device Management and Research SAIHST Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Two major fundus imaging types, conventional fundus (CF) imaging and ultrawidefield fundus (UF) imaging, are widely used in ophthalmology. In a typical clinical setting, both imaging methods are used simultaneously to diagnose diseases depending on the case. This study introduced an enhanced method of modality transformation to boost the diagnostic support capabilities of deep-learning models in ophthalmology. The method proposed in this study to translate UF images (UFIs) to CF images (CFIs) can replace the common clinical approach of both imaging methods, thereby reducing financial and temporal burdens on patients. This study learns and transforms the relationship between these modalities using UFI–CFI image pairs obtained from the same patient on the same day to minimize distorted information and accurately convert between different modalities. Our proposed model uses scale-aware registration and gradient-guided translation methods to align the UFI–CFI pairs, The generated CFIs not only enhance the quality of images and better represent the retinal area compared to existing methods but also effectively preserve the disease-related details in UFIs, aiding in the diagnosis. In addition, we optimize the image transformation performance, resulting in an 22% improvement in image quality assessment scales compared with previous methods. Furthermore, we acquired the observation that our results are nearly impossible to discern any differences between the real CFIs and the generated CFIs in the view of ophthalmologists. © 2024, The Authors. All rights reserved.

关键词： Ophthalmology

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Visual Change Detection in Multi-Temporal Vegetation Transects of Alpine Plants 10

Visual Change Detection in Multi-Temporal Vegetation Transec...

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10th International Workshop on Simulation for Energy, Sustainable Development and Environment, SESDE 2022

作者： Pritz, Sebastian Praschl, Christoph Kaiser, Roland Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Department for Medical and Bioinformatics School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria ENNACON Environment Nature Consulting KG Altheim 13 Feldkirchen bei Mattighofen5143 Austria Department Environment and Biodiversity Paris-Lodron University Salzburg Hellbrunner Str. 34 Salzburg5020 Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria

ISBN: (纸本)9788885741836

Due to the apparent effects of climate change on the Earth's ecosystems, it is more important than ever to monitor flora and fauna in affected regions, e.g. mountain areas above the tree line. In the alpine ecosystem, and not just there, Vegetation plays a fundamental role and is the subject of this study. The work aims to develop algorithms for recognising small stature alpine plants from close range top view images. Ideally, automated assessment algorithms of the plant cover should objectively help scientists observe and interpret the state of the plant ecosystem over a long time series. Therefore, the aim in this respect was to derive visualisations that accurately describe plant growth and displacement (translocation). Additionally, recording changes in biodiversity was an intent. This work uses multi-temporal data comprising RGB images and multi-label masks to accomplish the aforementioned task. The evaluated methods involve mask comparison, optical flow estimation, detection of individual plants, and descriptive statistical analysis of image feature properties. Tests on the given data set show that all methods but the optical flow estimation have great potential. The mask comparison method captured plant growth and translocation most satisfactory. Individual plant detection and statistical analysis further helped to evaluate changes in biodiversity. When combined, the proposed methods give an immediate overview about relevant changes in the multi-temporal transects, which has not been done before for close-distance images of alpine plants. © 2022 The Authors

关键词： Ecosystems

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Need for Computational and Psycho-linguistics Models in Natural Language Processing for Web Documents 4

Need for Computational and Psycho-linguistics Models in Natu...

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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Naqvi, Muhammad Raza Shahzad, Syed Khuram Iqbal, Muhammad Waseem Ahmed, Muhammad Tahir, Muhammad Usman Khan, Babar Ayub Superior College Department of Computer Science Lahore Pakistan University of Management Technology Department of Informatics and Systems Lahore Pakistan Superior College Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9781728190907

Natural Language Processing (NLP) is an argument that provokes territory of figural manner for motorized scrutiny and depiction of humane language. Research in it unfolded from the term of punch cards till the age of intelligent examination engines which takes less than a second to Figure out millions of web pages and digital documents. In the web, pieces of evidence and end-users produced content to have formerly stimulated text. The obligation of advantageous and astute content driving is decisive forthwith. Consequently, the web is being treated as a foremost source of evidence brought about the extensive extent of a probe to perceive the compass of the machine. Considering the democratization of online content formulation, it has skeptically influenced the information resurgence and eradication. This paper pulls contemporary developments in the NLP probe to perceive the scope of machine learning in a modern era to avoid ramification of adverse instructions. © 2020 IEEE.

关键词： Computational and psycholinguistics models Cortana Google Assistant ALO Natural language processing Siri Web documents

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Multi-sector Risk Management Framework for Analysis Cybersecurity Challenges and Opportunities 1

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11th International Conference on Multimedia Communications, Services and Security, MCSS 2022

作者： Niemiec, Marcin Pappalardo, Salvatore Marco Bozhilova, Maya Stoianov, Nikolai Dziech, Andrzej Stiller, Burkhard AGH University of Science and Technology Mickiewicza 30 Krakow30-059 Poland CIRM Viale Ortles 22 Milan Italy Software Engineering Italia S.r.l. Piazza Abramo Lincoln 7 Catania Italy Bulgarian Defence Institute Sofia Bulgaria Communication Systems Group CSG Department of Informatics IfI University of Zurich UZH Binzmühlestrasse 14 Zürich8050 Switzerland

ISBN: (数字)9783031202155

ISBN: (纸本)9783031202148

This paper describes an approach to analyse transversal and inter-sectoral cybersecurity challenges and opportunities: dedicated risk assessment and management framework, which can be used to develop cybersecurity technology roadmaps. This multi-sector assessment framework is able to prioritise and evaluate cybersecurity risks in trans-sectoral and inter-sectoral contexts as well as supports proper resource allocations and mitigation actions. To achieve this goal, the analysis of known risk management and risk assessment frameworks was performed, and results are presented in this paper. Also, an overview on transversal, inter-sectoral and multi-sectoral technological challenges and opportunities is provided. The result of this analysis is an architecture of the ECHO Multi-sector Assessment Framework, which was described in detail, including identified and analysed transversal aspects, multi-sector dependencies, and technological challenges and opportunities determine the input data for the framework. This solution is applicable in many sectors, such as energy, healthcare, maritime transportation, or defence, however it can also be extended to others. The architecture of the framework proposed supports the design of cybersecurity technology roadmap and the definition of governance models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Risk assessment

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Basic Space Mapping: A Retrospective and its Application to Design Optimization of Nonlinear RF and Microwave Circuits

Basic Space Mapping: A Retrospective and its Application to ...

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European Conference on Microwave

作者： José E. Rayas-Sánchez John W. Bandler Department of Electronics Systems and Informatics ITESO The Jesuit University of Guadalajara Mexico Microwave Harmonica User’s Manual Compact Software Inc. Paterson Department of Electrical and Computer Engineering McMaster University Hamilton Canada OSA90 Optimization Systems Associates Dundas Canada Canada

ISBN: (数字)9782874870699

ISBN: (纸本)9781665458818

Space mapping (SM) is one of the most powerful and computationally efficient design optimization methodologies in RF and microwave engineering. Its impressive evolution in terms of algorithmic variations and diverse engineering applications is well-documented. Most of the SM-based design optimization cases, including those solved by advanced and sophisticated space mapping formulations, have been demonstrated for linear frequency-domain microwave circuits. In this paper, we provide a brief retrospective on the emergence of space mapping, including its initial impression on a worldwide authority on nonlinear microwave circuit simulation and design: Prof. Vittorio Rizzoli. We briefly review some of the most fundamental space mapping optimization concepts and emphasize their applicability to nonlinear transient-domain microwave circuit design optimization. We illustrate this by a typical problem of high-speed digital signal conditioning: the physical design of a set of CMOS inverters driving an FR4 printed circuit board interconnect.

关键词： Semiconductor device modeling Radio frequency Printed circuits Integrated circuit interconnections Voltage Microwave circuits Microwave theory and techniques

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