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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Procedia Computer science 2023年 218卷 24-34页

作者： Seifedine Kadry Rubén González Crespo Enrique Herrera-Viedma Sujatha Krishnamoorthy Venkatesan Rajinikanth Faculty of Applied Computing and Technology Noroff University College Kristiansand 94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de La Rioja 26006 Andalusian Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai 602105 Tamil Nadu India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores.

关键词： Breast cancer Thermal imaging Firefly algorithm Feature optimization Classification

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Risk-based decision making: estimands for sequential prediction under interventions

arXiv

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arXiv 2023年

作者： Luijken, Kim Morzywolek, Pawel van Amsterdam, Wouter Cinà, Giovanni Hoogland, Jeroen Keogh, Ruth Krijthe, Jesse Magliacane, Sara van Ommen, Thijs Peek, Niels Putter, Hein van Smeden, Maarten Sperrin, Matthew Wang, Junfeng Weir, Daniala Didelez, Vanessa van Geloven, Nan Department of Epidemiology Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Department of Statistics University of Washington Seattle United States Department of Medical Informatics Amsterdam University Medical Centers Amsterdam Netherlands Institute for Logic Language and Computation University of Amsterdam Amsterdam Netherlands Pacmed Amsterdam Netherlands Department of Epidemiology and Data Science Amsterdam University Medical Centers Amsterdam Netherlands Department of Medical Statistics London School of Hygiene & Tropical Medicine Keppel Street London United Kingdom Pattern Recognition and Bio-Informatics Group EEMCS Delft University of Technology Delft Netherlands Amsterdam Machine Learning Lab University of Amsterdam Amsterdam Netherlands Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology Department of Pharmaceutical Sciences Utrecht University Utrecht Netherlands Department of Biometry and Data Management Leibniz Institute for Prevention Research Epidemiology - BIPS Bremen Germany Faculty of Mathematics/Computer Science University of Bremen Bremen Germany

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions. © 2023, CC BY.

关键词： Forecasting

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Povm-Based Quantum Self-Attention Neural Network

Povm-Based Quantum Self-Attention Neural Network

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Jiachun Wei Zhimin He Chuangtao Chen Maijie Deng Haozhen Situ School of Mathematics and Big data Foshan University Foshan China School of Electronic and Information Engineering Foshan University Foshan China Faculty of Innovation Engineering Macau University of Science and Technology Macau China School of Mechatronic Engineering and Automation Foshan University Foshan China College of Mathematics and Informatics South China Agricultural University Guangzhou China

Quantum Self-Attention Neural Network (QSANN) has demonstrated remarkable potential. However, it is limited to measuring only partial information from qubits, thereby restricting its information extraction capability. Moreover, the measurement process requires multiple measurements for each qubit, leading to inefficiency in utilizing the feature space. To address these challenges, this paper introduces a novel approach called Positive Operator-Valued Measure based Quantum Self-Attention Neural Network (POVM-QSANN). It leverages POVM operators as observables, enabling the extraction of comprehensive QKV (Query-Key-Value) feature vectors from each qubit. This innovative mechanism significantly enhances information extraction capability and efficiently utilizes the feature space. The experimental results underscore the considerable advancements of POVM-QSANN on MC and RP datasets. Particularly noteworthy is its remarkable accuracy increase of 9.68% compared to QSANN on the RP dataset.

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Blockchain and cryptocurrencies: A classification and comparison of architecture drivers

Blockchain and cryptocurrencies: A classification and compar...

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作者： Garriga, Martin Dalla Palma, Stefano Arias, Maxmiliano De Renzis, Alan Pareschi, Remo Andrew Tamburri, Damian Department of Management Tilburg School of Economics and Management Tilburg Netherlands JADE Lab Jheronimus Academy of Data Science Den Bosch Netherlands CONICET Faculty of Informatics National University of Comahue Neuquen Argentina Fidtech Neuquen Argentina Department of Biosciences and Territory University of Molise Molise Italy Technical Univ. of Eindhoven Eindhoven Netherlands

Blockchain is a decentralized transaction and data management solution, the technological leap behind the success of Bitcoin and other cryptocurrencies. As the variety of existing blockchains and distributed ledgers continues to increase, adopters should focus on selecting the solution that best fits their needs and the requirements of their decentralized applications, rather than developing yet another blockchain from scratch. In this article we present a conceptual framework to aid software architects, developers, and decision makers to adopt the right blockchain technology. The framework exposes the interrelation between technological decisions and architectural features, capturing the knowledge from existing academic literature, industrial products, technical forums/blogs, and experts' feedback. We empirically show the applicability of our framework by dissecting the platforms behind Bitcoin and other top 10 cryptocurrencies, aided by a focus group with researchers and industry practitioners. Then, we leverage the framework together with key notions of the architectural tradeoff analysis method to analyze four real-world blockchain case studies from industry and academia. Results shown that applying our framework leads to a deeper understanding of the architectural tradeoffs, allowing to assess technologies more objectively and select the one that best fit developers' needs, ultimately cutting costs, reducing time-to-market and accelerating return on investment. © 2020 The Authors. Concurrency and Computation: Practice and Experience published by John Wiley & Sons Ltd.

关键词： Software architecture

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An Overview and Recent Developments in the Analysis of Multistate Processes

arXiv

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arXiv 2025年

作者： Gorfine, Malka Cook, Richard J. Andersen, Per Kragh Therneau, Terry M. Joly, Pierre Putter, Hein Perme, Maja Pohar Abrahamowicz, Michal Department of Statistics and Operations Research Tel Aviv University Israel Department of Statistics and Actuarial Science University of Waterloo Canada Section of Biostatistics University of Copenhagen Denmark Division of Biomedical Statistics and Informatics Mayo Clinic New York United States Inserm ISPED Bordeaux Populations Health Research Center University of Bordeaux France Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Biostatistics and Medical Informatics Medical Faculty University of Ljubljana Slovenia Department of Epidemiology Biostatistics and Occupational Health McGill University MontrealQC Canada

Multistate models offer a powerful framework for studying disease processes and can be used to formulate intensity-based and more descriptive marginal regression models. They also represent a natural foundation for the construction of joint models for disease processes and dynamic marker processes, as well as joint models incorporating random censoring and intermittent observation times. This article reviews the ways multistate models can be formed and fitted to life history data. Recent work on pseudo-values and the incorporation of random effects to model dependence on the process history and between-process heterogeneity are also discussed. The software available to facilitate such analyses is listed. © 2025, CC BY.

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ATTENTION SWIN U-NET: CROSS-CONTEXTUAL ATTENTION MECHANISM FOR SKIN LESION SEGMENTATION

arXiv

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arXiv 2022年

作者： Aghdam, Ehsan Khodapanah Azad, Reza Zarvani, Maral Merhof, Dorit Department of Electrical Engineering Shahid Beheshti University Tehran Iran Institute of Imaging and Computer Vision RWTH Aachen University Aachen Germany Department of Computer Engineering Alzahra University Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany

Melanoma is caused by the abnormal growth of melanocytes in human skin. Like other cancers, this life-threatening skin cancer can be treated with early diagnosis. To support a diagnosis by automatic skin lesion segmentation, several Fully Convolutional Network (FCN) approaches, specifically the U-Net architecture, have been proposed. The U-Net model with a symmetrical architecture has exhibited superior performance in the segmentation task. However, the locality restriction of the convolutional operation incorporated in the U-Net architecture limits its performance in capturing long-range dependency, which is crucial for the segmentation task in medical images. To address this limitation, recently a Transformer based U-Net architecture that replaces the CNN blocks with the Swin Transformer module has been proposed to capture both local and global representation. In this paper, we propose Att-SwinU-Net, an attention-based Swin U-Net extension, for medical image segmentation. In our design, we seek to enhance the feature re-usability of the network by carefully designing the skip connection path. We argue that the classical concatenation operation utilized in the skip connection path can be further improved by incorporating an attention mechanism. By performing a comprehensive ablation study on several skin lesion segmentation datasets, we demonstrate the effectiveness of our proposed attention mechanism. © 2022, CC BY.

关键词： Diagnosis

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Revealing Key Amino Acids on HMGB1 Box A Domain which can be used for Peptide Targeting

引用

Neuroscience Applied 2024年 3卷

作者： O.M. Apaydin O. Sensoy S. Baday Istanbul Technical University Institute of Computer Science Department Informatics Istanbul Türkiye Istanbul Technical University Department of Artificial Intelligence and Data Engineering- Faculty of Computer Informatics and Engineering Istanbul Türkiye

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EDCOPA Protocol: Addressing Scalability, High-Density and Load Balancing Challenges in WSN-based IoT

EDCOPA Protocol: Addressing Scalability, High-Density and Lo...

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Electrical, Computer, Telecommunication and Energy Technologies (ECTE-Tech), International Conference on

作者： Foudil Mir Abderrazak Sebaa Abdelkader Laouid Ahcene Bounceur Farid Meziane Faculty of Exact Sciences LIMED Laboratory University of Bejaia Bejaia Algeria Laboratoire LITAN Ecole Supérieure en Sciences et Technologies de l'Informatique et du Numérique Bejaia Algerie LIAP Laboratory University of El Oued El Oued Algeria College of Computing and Informatics University of Sharjah Sharjah UAE Data Science Research Centre University of Derby Derby UK

ISBN: (数字)9798350388480

ISBN: (纸本)9798350388497

Energy optimization in Wireless Sensor Networks for the Internet of Things (WSN-Based IoT) is essential to extend the lifetime of networks while guaranteeing optimum performance. This paper investigates the performance of the EDCOPA protocol, which is an enhancement of the DCOPA protocol, by addressing three key aspects: the impact of scalabil-ity, network density, and load balancing. The DCOPA protocol, while powerful, has limitations in that it relies solely on residual energy and Distance to the Base Station (DistBS) to select cluster heads (CHs). To overcome this limitation, the EDCOPA protocol integrates new criteria, namely the energy consumption in the previous round Epr i and the CH role frequency Nch i of a given node. We study the effect of scalability as well as the impact of density on the Lifetime Parameters (LTP). In addition, we analyze load balancing by evaluating the distribution of CHs and clusters after the clustering process. This analysis examines how the proposed improvements affect energy efficiency and network LTP. This paper provides an in-depth look at the performance of the EDCOPA protocol in terms of properties that are extremely important in WSN-based IoT networks.

关键词： Wireless sensor networks Energy consumption Protocols Scalability Voting Load management Robustness Energy efficiency Internet of Things Optimization

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MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence

arXiv

引用

arXiv 2021年

作者： Giziński, Stanislaw Kuźba, Michal Pieliński, Bartosz Sienkiewicz, Julian Laniewski, Stanislaw Biecek, Przemyslaw MI2 Data Lab Faculty of Mathematics and Information Science Warsaw University of Technology Faculty of Mathematics Informatics and Mechanics University of Warsaw Faculty of Political Science and International Studies University of Warsaw Faculty of Physics Warsaw University of Technology Quantitative Psychology and Economics Faculty of Economics University of Warsaw

The growing number of AI applications, also for high-stake decisions, increases the interest in Explainable and Interpretable Machine Learning (XI-ML). This trend can be seen both in the increasing number of regulations and strategies for developing trustworthy AI and the growing number of scientific papers dedicated to this topic. To ensure the sustainable development of AI, it is essential to understand the dynamics of the impact of regulation on research papers as well as the impact of scientific discourse on AI-related policies. This paper introduces a novel framework for joint analysis of AI-related policy documents and eXplainable Artificial Intelligence (XAI) research papers. The collected documents are enriched with metadata and interconnections, using various NLP methods combined with a methodology inspired by Institutional Grammar. Based on the information extracted from collected documents, we showcase a series of analyses that help understand interactions, similarities, and differences between documents at different stages of institutionalization. To the best of our knowledge, this is the first work to use automatic language analysis tools to understand the dynamics between XI-ML methods and regulations. We believe that such a system contributes to better cooperation between XAI researchers and AI policymakers. Copyright © 2021, The Authors. All rights reserved.

关键词： Planning

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‘Something that Facilitates Our Work…Rather than a Hindrance’: Clinical Pharmacist Perspectives on the Use of data Analytics to Support Polypharmacy Medication Review In Primary Care

SSRN

引用

SSRN 2022年

作者： Hurley, Ruth Jury, Francine van Staa, Tjeerd P. Palin, Victoria Armitage, Christopher J. Manchester Centre for Health Psychology Division of Psychology and Mental Health School of Health Sciences Faculty of Biology Medicine and Health The University of Manchester United Kingdom Centre for Health Informatics Division of Informatics Imaging and Data Science School of Health Sciences Faculty of Biology Medicine and Health The University of Manchester United Kingdom NIHR Greater Manchester Patient Safety Translational Research Centre The University of Manchester United Kingdom

Background: Polypharmacy and overprescribing pose an enormous challenge to safe healthcare and efficient use of resources. Patient record data could inform safer prescribing and deprescribing, but it is unclear how these complex data should be summarised and displayed to clinicians. The current study examined the perspectives of clinical pharmacists (CPs), a newly expanding workforce of primary care medication specialists, to explore ways that novel analytics could help improve health outcomes for frail and elderly (>65yrs) ***: The utility of novel analytics interventions were discussed in an exploratory scoping workshop. Health risk data for frail and elderly patients with polypharmacy (modelled from extensive national datasets) were presented to primary care clinical pharmacists (n=14). Verbal and textual comments were thematically analysed using the framework method (exploratory content analysis) combining inductive and deductive approaches. Results: Overarching themes of data use, data reservations and digital tools acceptance factors were identified. Respondents highlighted several uses for polypharmacy analytics interventions, including increased knowledge of clinical effects of drug-drug interactions, the ability to prioritise high-risk patients for reviews and medication to deprescribing (e.g., highlighting cumulative medication risk). data reservations were linked to existing barriers (such as cognitive overload from existing systems and patient explainability) meaning that CPs’ acceptance of digital analytics tools is heavily contingent on facilitators such as ease of use, clear targeted purpose and the ability to support clinicians’ understanding and confidence in evaluation of analytics for patient care decisions. Conclusion: The workshop helped to identify promising analytics and features for polypharmacy intervention development. Patient record data could help address a concerning deficit in evidence of real-world medication interactions,

关键词： data Analytics

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