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A Novel Cluster Detection of COVID-19 Patients and Medical Disease Conditions Using Improved Evolutionary Clustering Algorithm Star

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arXiv 2021年

作者： Hassan, Bryar A. Rashid, Tarik A. Hamarashid, Hozan K. Department of Computer Networks Technical College of Informatics Sulaimani Polytechnic University Sulaimani46001 Iraq Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler Iraq Information Technology Department Computer Science Institute Sulaimani Polytechnic University Sulaimani46001 Iraq

With the increasing number of samples, the manual clustering of COVID-19 and medical disease data samples becomes time-consuming and requires highly skilled labour. Recently, several algorithms have been used for clustering medical datasets deterministically;however, these definitions have not been effective in grouping and analysing medical diseases. The use of evolutionary clustering algorithms may help to effectively cluster these diseases. On this presumption, we improved the current evolutionary clustering algorithm star (ECA*), called iECA*, in three manners: (i) utilising the elbow method to find the correct number of clusters;(ii) cleaning and processing data as part of iECA* to apply it to multivariate and domain-theory datasets;(iii) using iECA* for real-world applications in clustering COVID-19 and medical disease datasets. Experiments were conducted to examine the performance of iECA* against state-of-the-art algorithms using performance and validation measures (validation measures, statistical benchmarking, and performance ranking framework). The results demonstrate three primary findings. First, iECA* was more effective than other algorithms in grouping the chosen medical disease datasets according to the cluster validation criteria. Second, iECA* exhibited the lower execution time and memory consumption for clustering all the datasets, compared to the current clustering methods analysed. Third, an operational framework was proposed to rate the effectiveness of iECA* against other algorithms in the datasets analysed, and the results indicated that iECA* exhibited the best performance in clustering all medical datasets. Further research is required on real-world multi-dimensional data containing complex knowledge fields for experimental verification of iECA* compared to evolutionary algorithms. © 2021, CC BY-NC-ND.

关键词： Coronavirus

Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models

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arXiv 2021年

作者： Sadiq, Ismail Perez-Alday, Erick A. Shah, Amit J. Rad, Ali Bahrami Sameni, Reza Clifford, Gari D. Department of Electrical & Computer Engineering Georgia Institute of Technology United States Department of Biomedical Informatics Emory University United States Department of Epidemiology Rollins School of Public Health Emory University United States Department of Biomedical Engineering Georgia Institute of Technology Emory University United States

MSC Codes 92C30, 92C32, 03H10, 62H30, 68Q07, 8T07, 78-10, 92-10, 62R07, 68T09, 68T10Objective: Transfer learning has been shown to be an effective strategy for pre-training a deep neural network (DNN) to boost performance on smaller data-sets. This is particularly useful when it is difficult or costly to acquire high quality data, such as in medicine. In this study we evaluated the additional utility of pre-training on a computationally efficient but realistic physiological model which can be tuned to explore a wide variety of states for a specific condition. In this current work, we focused on the existence of T wave alternans (TWAs) as a marker of post traumatic stress disorder (PTSD) in a small cohort of 36 veteran twins. Approach: Using a previously validated artificial ECG model, we generated 180,000 artificial ECGs with or without TWAs, with varying heart rate, breathing rate, TWA amplitude, and ECG morphology. We then took a previously developed state-of-the-art DNN, trained on over 70,000 patients to classify 25 different rhythms, modified the output layer to a binary class (TWA or no-TWA, or equivalently, PTSD or no-PTSD), and performed transfer learning on the artificial data. In a final transfer learning step, real the DNN was trained on ECGs from 12 individuals with PTSD and 24 controls, and evaluated using leave-one-subject-out cross-validation. The training and testing processes were repeated with and without each of the three data sets (rhythm data, artificial data, PTSD data) to evaluate the contribution of each transfer learning step. The area under receiver operating characteristic (AUROC) curve, accuracy (Acc), F1-score for PTSD and balanced accuracy (BAcc.) classification was reported for each trained model. Main results: The best performing approach (AUROC = 0.77, Acc = 0.72, F1-score = 0.64, BAcc. = 0.73) was found by performing both transfer learning steps, using the pre-trained arrhythmia DNN, the artificial data and the real PTSD-related ECG

关键词： Electrocardiography

Hybrid Optimization Method Using Simulated-Annealing-Based Ising Machine and Quantum Annealer

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arXiv 2023年

作者： Kikuchi, Shuta Togawa, Nozomu Tanaka, Shu Department of Applied Physics and Physico-Informatics Keio University Kanagawa223-8522 Japan Department of Computer Science and Communications Engineering Waseda University Tokyo169-8555 Japan Keio University Tokyo108-8345 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Research Frontiers Initiative Tokyo Institute of Technology Tokyo108-0023 Japan

Ising machines have the potential to realize fast and highly accurate solvers for combinatorial optimization problems. They are classified based on their internal algorithms. Examples include simulated-annealing-based Ising machines (non-quantum-type Ising machines) and quantum-annealing-based Ising machines (quantum annealers). Herein we propose a hybrid optimization method, which utilizes the advantages of both types. In this hybrid optimization method, the preprocessing step is performed by solving the non-quantum-annealing Ising machine multiple times. Then sub-Ising models with a reduced size by spin fixing are solved using a quantum annealer. The performance of the hybrid optimization method is evaluated via simulations using Simulated Annealing (SA) as a non-quantum-type Ising machine and D-Wave Advantage as a quantum annealer. Additionally, we investigate the parameter dependence of the proposed hybrid optimization method. The hybrid optimization method outperforms the preprocessing SA and the quantum annealing machine alone in fully connected random Ising models. Copyright © 2023, The Authors. All rights reserved.

关键词： Simulated annealing

Towards deployment-centric multimodal AI beyond vision and language

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arXiv 2025年

作者： Liu, Xianyuan Zhang, Jiayang Zhou, Shuo van der Plas, Thijs L. Vijayaraghavan, Avish Grishina, Anastasiia Zhuang, Mengdie Schofield, Daniel Tomlinson, Christopher Wang, Yuhan Li, Ruizhe van Zeeland, Louisa Tabakhi, Sina Demeocq, Cyndie Li, Xiang Das, Arunav Timmerman, Orlando Baldwin-McDonald, Thomas Wu, Jinge Bai, Peizhen Al Sahili, Zahraa Alwazzan, Omnia Do, Thao N. Suvon, Mohammod N.I. Wang, Angeline Cipolina-Kun, Lucia Moretti, Luigi A. Farndale, Lucas Jain, Nitisha Efremova, Natalia Ge, Yan Varela, Marta Lam, Hak-Keung Celiktutan, Oya Evans, Ben R. Coca-Castro, Alejandro Wu, Honghan Abdallah, Zahraa S. Chen, Chen Danchev, Valentin Tkachenko, Nataliya Lu, Lei Zhu, Tingting Slabaugh, Gregory G. Moore, Roger K. Cheung, William K. Charlton, Peter H. Lu, Haiping Centre for Machine Intelligence University of Sheffield Sheffield United Kingdom School of Computer Science University of Sheffield Sheffield United Kingdom The Alan Turing Institute London United Kingdom Department of Metabolism Digestion and Reproduction Imperial College London London United Kingdom Department of Applied AI Simula Research Laboratory Oslo Norway Information School University of Sheffield Sheffield United Kingdom NHS England Leeds United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Engineering King’s College London London United Kingdom Department of Computing Science University of Aberdeen Aberdeen United Kingdom School of Informatics University of Edinburgh Edinburgh United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom Department of Informatics King’s College London London United Kingdom Department of Earth Sciences University of Cambridge Cambridge United Kingdom Department of Computer Science University of Manchester Manchester United Kingdom Department of Computer Science Queen Mary University of London London United Kingdom Digital Environment Research Institute Queen Mary University of London London United Kingdom Department of Computer Science University of Bath Bath United Kingdom Department of Classics King’s College London London United Kingdom School of Electrical Electronic and Mechanical Engineering University of Bristol Bristol United Kingdom School of Engineering University of the West of England Bristol United Kingdom Cancer Research UK Scotland Institute Glasgow United Kingdom School of Business and Management Queen Mary University of London London United Kingdom City St George’s University of London London United Kingdom British Antarctic Survey Cambridge United Kingdom School of Health and Wellbeing University of Glasgow Glasgow United Kingdom Chief Data & AI Office Lloyds Banking Group Lo

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-making across disciplines such as healthcare, science, and engineering. However, most multimodal AI advances focus on models for vision and language data, while their deployability remains a key challenge. We advocate a deployment-centric workflow that incorporates deployment constraints early to reduce the likelihood of undeployable solutions, complementing data-centric and model-centric approaches. We also emphasise deeper integration across multiple levels of multimodality and multidisciplinary collaboration to significantly broaden the research scope beyond vision and language. To facilitate this approach, we identify common multimodal-AI-specific challenges shared across disciplines and examine three real-world use cases: pandemic response, self-driving car design, and climate change adaptation, drawing expertise from healthcare, social science, engineering, science, sustainability, and finance. By fostering multidisciplinary dialogue and open research practices, our community can accelerate deployment-centric development for broad societal impact. © 2025, CC BY.

关键词： Decision making

Middle East and North African Health informatics Association (MENAHIA): Technological initiatives for 'One Health'

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Yearbook of Medical informatics 2023年第1期32卷 320-329页

作者： Abd-Alrazaq, Alaa Al-Shorbaji, Najeeb Lakhdari, Kheira Elbokl, Ahmed Farouk, Hani Wahba, Hoda Elgasser, Naema Mohell, Rania Emara, Tamer Ayatollahi, Haleh Kalhori, Sharareh R. Niakan Rabiei, Reza Tara, Mahmood Akhu-Zaheya, Laila Al-Qutob, Raeda Alrazak, Sadam Alabed Al-Jafar, Eiman Alhuwail, Dari Ghazal, Hassan Dehbi, Zineb El Otmani Al Idrissi, Najib Fihri, Ouafaa Fassi Belyamani, Lahcen Shahzad, Tariq Ahmed, Zakiuddin Ahmad, Arfan Shah, Zubair Hamra, Eman Abu Taweel, Adel Nashwan, Abdulqadir J. Househ, Mowafa Hamdi, Mounir Al-Thani, Dena Alam, Tanvir Alshammari, Abdulwahhab Alnafrani, Sana Alali, Haitham AI Center for Precision Health Weill Cornell Medicine-Qatarc Doha Qatar EHealth Development Association Amman Jordan Abou Bekr Belkaid University of Tlemcen Chetouane Algeria Ain Shams University Virtual Hospital Ain Shams University Cairo Egypt Ain Shams University Cairo Egypt World Health Organization Cairo Egypt Alexandria Regional Blood Transfusion Center Alexandria University Alexandria Egypt Health Management and Economics Research Center Health Management Research Institute Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Health Management and Information Sciences Iran University of Medical Sciences Tehran Iran Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran Peter L. Reichertz Institute for Medical Informatics (PLRI) Technical University of Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciencesc Tehran Iran Department of Medical Informatics Faculty of Medicine Mashhad University of Medical Sciencesc Mashhad Iran School of Nursing Jordan University of Science and Technology Jordan Jordan Centre for Disease Control and Prevention Amman Jordan Faculty of Applied Science and Engineering University of Toronto Toronto Canada Kuwait Health Informatics Association Kuwait Information Science Department College of Life Sciences Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait Mohammed VI University of Health Sciences Casablanca Morocco National Center for Scientific and Technical Research (CNRST) Rabat Morocco Hassan II Agronomic and Veterinary Institute Rabat Morocco COMSATS University Islamabad Sahiwal Campus Islamabad Pakistan Digital Care Karachi Pakistan College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Al Quds Unive

MENAHIA (Middle East and North African Health informatics Association) is the International Medical informatics Association chapter dedicated to the Middle East and North Africa region. This region is rapidly growing in terms of the use of health informatics or what has been recently coined digital health. Human health is highly affected by the health of the environment, animal health, food, nutrition, climate change, and many other factors that are beyond the biological or genetic structure of human beings. The impact of animal health and the health of the environment on people's health is an old phenomenon but recent reemerging and appearance of diseases have clearly demonstrated the link between these. The Novel Coronavirus disease (COVID-19) that almost all of us have been suffering from is an example of this. A number of countries in the region have already shown the depth and the work that they do to integrate the concept of 'One Health' in the public health surveillance system as they have described the work that has been done to capture data from databases other than those dealing with human beings. The examples that were provided to monitor the health of animals, agriculture, environmental health, climate change, and man-made and natural disasters are just examples of what countries have been registering in their databases and informing the health authorities of these changes and emerging trends. © The Author(s) 2023.

关键词： 'One Health' 'One Health' informatics Algeria Artificial Intelligence Climate Change Digital Health Egypt Health informatics Health Information Technologies International Medical informatics Association (IMIA) Iran Jordan Kuwait MENAHIA Middle East and North Africa Morocco Pakistan Palestine Qatar Saudi Arabia United Arab Emirates Zoonitic Diseases

DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical Coherence Tomography Angiography Images

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arXiv 2023年

作者： Qian, Bo Chen, Hao Wang, Xiangning Che, Haoxuan Kwon, Gitaek Kim, Jaeyoung Choi, Sungjin Shin, Seoyoung Krause, Felix Unterdechler, Markus Hou, Junlin Feng, Rui Li, Yihao Daho, Mostafa El Habib Wu, Qiang Zhang, Ping Yang, Xiaokang Cai, Yiyu Li, Huating Jia, Weiping Sheng, Bin Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong Department of Ophthalmology Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China VUNO Inc. Seoul Korea Republic of AI/DX Convergence Business Group KT Korea Republic of Johannes Kepler University Linz Linz Austria School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University China Academy for Engineering and Technology Fudan University China LaTIM UMR 1101 Inserm Brest France University of Western Brittany Brest France Department of Computer Science and Engineering The Ohio State University Ohio United States Department of Biomedical Informatics The Ohio State University Ohio United States Translational Data Analytics Institute The Ohio State University Ohio United States MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Shanghai China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Department of Endocrinology and Metabolism Shanghai Jiao Tong University Affiliated Sixth People’s Hospital Shanghai China Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China

computer-assisted automatic analysis of diabetic retinopathy (DR) is of great importance in reducing the risks of vision loss and even blindness. Ultra-wide optical coherence tomography angiography (UW-OCTA) is a non-invasive and safe imaging modality in DR diagnosis system, but there is a lack of publicly available benchmarks for model development and evaluation. To promote further research and scientific benchmarking for diabetic retinopathy analysis using UW-OCTA images, we organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and computer Assisted Intervention (MICCAI 2022). The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams from geographically diverse institutes submitting different solutions in these three tasks, respectively. This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge. The obtained results from top algorithms indicate the importance of data augmentation, model architecture and ensemble of networks in improving the performance of deep learning models. These findings have the potential to enable new developments in diabetic retinopathy analysis. The challenge remains open for post-challenge registrations and submissions for benchmarking future methodology developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical tomography

Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit

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arXiv 2022年

作者： Chen, Weibin Sharifrazi, Danial Liang, Guoxi Band, Shahab S. Chau, Kwok Wing Mosavi, Amir College of Computer Science and Artificial Intelligence Wenzhou University Zhejiang Wenzhou325035 China Department of Computer Engineering Shiraz Branch Islamic Azad University Shiraz Iran Department of Artificial Intelligence Wenzhou Polytechnic Wenzhou325035 China Future Technology Research Center College of Future National Yunlin University of Science and Technology Yunlin County Taiwan Department of Civil and Environmental Engineering Hong Kong Polytechnic University Hong Kong John von Neumann Faculty of Informatics Obuda University Budapest Hungary Institute of Information Engineering Automation and Mathematics Slovak University of Technology Bratislava Slovakia

Streamlined weirs, as a nature-inspired type of weirs, has gained tremendous attention among hydraulic engineers mainly due to their well-known performance with high discharge coefficient. Computational fluid dynamic (CDF) is considered as a robust tool to predict discharge coefficient. To bypass the computational cost of CFD-based assessment, the present study proposes data-driven modelling techniques, as an alternative to CFD simulation, to predict discharge coefficient based on an experimental dataset. To this end, after splitting the dataset by k-fold cross-validation technique, the performance assessment of classical and hybrid machine-deep learning (ML-DL) algorithms is undertaken. Amongst ML techniques, linear regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and decision tree (DT) algorithms are studied . In the context of DL, long short-term memory (LSTM), convolutional neural network (CNN), gated recurrent unit (GRU) and their hybrid forms such as LSTM-GRU, CNN-LSTM and CNN-GRU techniques are compared by different error metrics. It is found that the proposed three-layer hierarchical DL algorithm consisting of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized by LR method (i.e., LR-CGRU), leads to lower error metrics. This paper paves the way for data-driven modelling of streamlined *** Codes 68T05 © 2022, CC BY.

关键词： Deep neural networks

Leveraging Generalizability of Image-to-Image Translation for Enhanced Adversarial Defense

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arXiv 2025年

作者： Zhang, Haibo Yao, Zhihua Sakurai, Kouichi Saitoh, Takeshi Department of Artificial Intelligence Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Faculty of Economics and Business Administration The University of Kitakyushu Japan Department of Informatics Faculty of Information Science and Electrical Engineering Kyushu University Japan

In the rapidly evolving field of artificial intelligence, machine learning emerges as a key technology characterized by its vast potential and inherent risks. The stability and reliability of these models are important, as they are frequent targets of security threats. Adversarial attacks, first rigorously defined by Ian Goodfellow et al. in 2013, highlight a critical vulnerability: they can trick machine learning models into making incorrect predictions by applying nearly invisible perturbations to images. Although many studies have focused on constructing sophisticated defensive mechanisms to mitigate such attacks, they often overlook the substantial time and computational costs of training and maintaining these models. Ideally, a defense method should be able to generalize across various, even unseen, adversarial attacks with minimal overhead. Building on our previous work on image-to-image translation-based defenses, this study introduces an improved model that incorporates residual blocks to enhance generalizability. The proposed method requires training only a single model, effectively defends against diverse attack types, and is well-transferable between different target models. Experiments show that our model can restore the classification accuracy from near zero to an average of 72% while maintaining competitive performance compared to state-of-the-art methods. Significantly, our model operates more efficiently, reducing the time needed to process individual images and speeding up the training process to achieve faster convergence. Robustness tests further confirm stable performance under varying attack strengths, demonstrating the model’s practical value in real-world adversarial settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks