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Automatic liver-vessel examination from CT slice using Kapur’s thresholding and watershed algorithm

Procedia Computer Science 2024年 235卷 1824-1831页

作者： Seifedine Kadry Laith Abualigah Rubén González Crespo Elena Verdú Robertas Damasevicius Vijendra Singh Venkatesan Rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Computerized medical image examination (CMIE) plays a significant role in modern hospitals to achieve the necessary tasks, like segmentation and classification. By segmenting an image, we can extract a particular section for examination. A two-dimensional computed tomography (CT) slice was used for liver-vessel examination (LiVE). A simple automatic technique for supporting LiVE is being developed in this research. A CT slice is collected, a 3D to 2D conversion is done, (ii) Kapur’s tri-level thresholding and Hummingbird-Optimizer is used to enhance the CT slice, (iii) the watershed algorithm (WA) is used to extract the vessel, and (iv) the WA is compared and verified against the segmentation methods chosen. WA provides better segmentation results than other methods because it is an automatic approach. Using the chosen image database, the proposed technique achieves an overall segmentation accuracy of >97%. Other segmentation problems can be used in the future to verify the merit of this scheme.

关键词： liver CT liver-vessel Kapur’s thresholding hummingbird-optimizer watershed-algorithm

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Improving Generalization of Metric Learning via Listwise Self-distillation

arXiv

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arXiv 2022年

作者： Zeng, Zelong Yang, Fan Wang, Zheng Satoh, Shin'ichi The Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan The Digital Content and Media Sciences Research Division National Institute of Informatics Japan The School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China

Most deep metric learning (DML) methods employ a strategy that forces all positive samples to be close in the embedding space while keeping them away from negative ones. However, such a strategy ignores the internal relationships of positive (negative) samples and often leads to overfitting, especially in the presence of hard samples and mislabeled samples. In this work, we propose a simple yet effective regularization, namely Listwise Self-Distillation (LSD), which progressively distills a model's own knowledge to adaptively assign a more appropriate distance target to each sample pair in a batch. LSD encourages smoother embeddings and information mining within positive (negative) samples as a way to mitigate overfitting and thus improve generalization. Our LSD can be directly integrated into general DML frameworks. Extensive experiments show that LSD consistently boosts the performance of various metric learning methods on multiple datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Distillation

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Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval

arXiv

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arXiv 2022年

作者： Zeng, Zelong Wang, Zheng Yang, Fan Satoh, Shin'ichi The School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University China The Department of Information and Communication Engineering Graduate School of Information Science and Technology The University of Tokyo Japan The Digital Content and Media Sciences Research Division National Institute of Informatics Japan

The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks. In this paper, we investigate an extremely challenging task: given a ground-view image of a landmark, we aim to achieve cross-view geo-localization by searching out its corresponding satellite-view images. Specifically, the challenge comes from the gap between ground-view and satellite-view, which includes not only large viewpoint changes (some parts of the landmark may be invisible from front view to top view) but also highly irrelevant background (the target landmark tend to be hidden in other surrounding buildings), making it difficult to learn a common representation or a suitable mapping. To address this issue, we take advantage of drone-view information as a bridge between ground-view and satellite-view domains. We propose a Peer Learning and Cross Diffusion (PLCD) framework. PLCD consists of three parts: 1) a peer learning across ground-view and drone-view to find visible parts to benefit ground-drone cross-view representation learning;2) a patch-based network for satellite-drone cross-view representation learning;3) a cross diffusion between ground-drone space and satellite-drone space. Extensive experiments conducted on the University-Earth and University-Google datasets show that our method outperforms state-of-the-arts significantly. Copyright © 2022, The Authors. All rights reserved.

关键词： Satellites

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A Novel Framework for Learning and Classifying the Imbalanced Multi-Label data

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Computer Systems Science & engineering 2024年第5期48卷 1367-1385页

作者： P.K.A.Chitra S.Appavu alias Balamurugan S.Geetha Seifedine Kadry Jungeun Kim Keejun Han Department of Computer Science and Engineering SRM Institute of Science and TechnologyTiruchirappalliTamil Nadu603203India Department of Computer Science and Engineering Periyar Maniammai Institute of Science&Technology(Deemed to be University)ThanjavurTamil Nadu613403India School of Computer Science and Engineering ChennaiTamil Nadu600048India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos10150Lebanon Department of Software Kongju National UniversityCheonan31080Republic of Korea Division of Computer Engineering Hansung UniversitySeoul02876Republic of Korea

A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label *** main objective of this work is to create a novel framework for learning and classifying imbalancedmulti-label *** work proposes a framework of two *** imbalanced distribution of themulti-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase ***,an adaptive weighted l21 norm regularized(Elastic-net)multilabel logistic regression is used to predict unseen samples in phase *** proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional *** minority labels in these samples are called difficult minority labels and are more prone to penalize classification *** concurrentmeasure is considered borderline,and labels associated with samples are regarded as borderline labels in the decision *** phase II,a novel adaptive l21 norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic *** on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods.

关键词： Multi-label imbalanced data multi-label learning Borderline MLSMOTE concurrent multi-label adaptive weighted multi-label elastic net difficult minority label

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A Lightweight and Robust Access Control Protocol for IoT-based e-Healthcare Network

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IEEE Transactions on Mobile Computing 2025年

作者： Ghaffar, Zahid Kuo, Wen-Chung Mahmood, Khalid Tariq, Tayyaba Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J.F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University Department of Computer Science and Engineering College of Informatics 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency. © 2002-2012 IEEE.

关键词： Authentication

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Is larger always better? Evaluating and prompting large language models for non-generative medical tasks

arXiv

引用

arXiv 2024年

作者： Zhu, Yinghao Gao, Junyi Wang, Zixiang Liao, Weibin Zheng, Xiaochen Liang, Lifang Wang, Yasha Pan, Chengwei Harrison, Ewen M. Ma, Liantao Institute of Artificial Intelligence Beihang University Beijing100191 China Centre for Medical Informatics University of Edinburgh EdinburghEH8 9YL United Kingdom Health Data Research UK United Kingdom School of Computer Science Peking University Beijing100871 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China ETH Zürich Zürich8092 Switzerland

Background: The deployment of Large Language Models (LLMs) in medical fields is increasing, but few research evaluate their ability to manage both structured Electronic Health Record (EHR) data and unstructured clinical notes. This study provides a comprehensive benchmark of various models, including GPT-based LLMs, BERT-based language models, and conventional clinical predictive models to determine their efficacy in non-generative medical tasks using widely recognized datasets. Methods: We evaluated 14 language models (9 GPT-based and 5 BERT-based) alongside 7 conventional clinical predictive models on the MIMIC dataset, which encompasses comprehensive ICU patient records, and the TJH dataset, which includes EHR data from early COVID-19 patients. The performance of these models was assessed through a series of both supervised and unsupervised tasks, including mortality and readmission predictions, disease hierarchy reconstruction, and biomedical sentence matching. The study focused on comparing zero-shot prediction capabilities of LLMs with conventional models and the effectiveness of finetuned BERT-based models in handling clinical texts. Results: LLMs demonstrated promising zero-shot predictive abilities on structured EHR data when equipped with well-designed prompting strategies, often surpassing traditional models trained with limited datasets. However, in tasks involving unstructured medical texts, LLMs did not outperform existing BERT-based models. Both in supervised and unsupervised scenarios, the performance of finetuned BERT models was superior, suggesting that the additional cost and complexity of implementing LLMs might not be justifiable for non-generative tasks. Conclusion: While LLMs show significant potential in interpreting structured EHR data through zero-shot learning, their application in processing unstructured medical texts is less advantageous compared to finetuned BERT models. The findings underscore the importance of selecting appropriate mo

关键词： Clinical research

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

arXiv

引用

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

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Simultaneous Super-Resolution and Denoising on MRI via Conditional Stochastic Normalizing Flow

Simultaneous Super-Resolution and Denoising on MRI via Condi...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhenhong Liu Xingce Wang Zhongke Wu Yi-Cheng Zhu Alejandro F. Frangi School of Artificial Intelligence Beijing Normal University Beijing China Department of Neurology Peking Union Medical College Hospital Beijing China Division of Informatics Imaging and Data Sciences School of Health Sciences Christabel Pankhurst Institute Department of Computer Science School of Engineering The University of Manchester UK Department of Cardiovascular Sciences Medical Imaging Research Centre (MIRC) Department of Electrical Engineering KU Leuven Leuven Belgium lan Turing Institute London UK

Magnetic resonance imaging (MRI) scans often suffer from noise and low-resolution (LR), which affect the diagnosis and treatment results obtained for patients. LR images and noise come together with MRI, and the existing methods solve image super-resolution (SR) reconstruction and denoising tasks in a step-by-step manner, which influences the overall real distribution of the MRI data. In this paper, we present a simultaneous SR and denoising algorithm based on a stochastic normalizing flow (SNF), named the MR image SR and denoising model based on an SNF (SRDSNF). SRDSNF adds the encoded information of the input image as the conditional information to each reverse step of the stochastic normalizing flow, which realizes a consistent description of the spatial distribution between the reconstruction result and the input image. We introduce rangenull space decomposition and subsequence sampling strategies to enhance the consistency of the input and output data and increase the generation speed of the model. Simultaneous SR and denoising tasks experiment is carried out using the BrainWeb and NFBS datasets. The experimental results show that good SR and denoising results are obtained with fewer sampling steps, these results are consistent with the ground truths, and the structural similarity and peak signal-to-noise ratio of the results are also higher than those of the comparison methods. The proposed method demonstrates potential clinical promise.

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Domain-invariant Clinical Representation Learning by Bridging data Distribution Shift across EMR datasets

arXiv

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arXiv 2023年

作者： Zhang, Zhongji Wang, Yuhang Zhu, Yinghao Ma, Xinyu Wang, Yasha Gao, Junyi Ma, Liantao Tang, Wen Zhang, Xiaoyun Wang, Ling Affiliated Xuzhou Municipal Hospital of Xuzhou Medical University Jiangsu China Peking University School Hospital of Stomatology Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Peking University Third Hospital Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China Centre for Medical Informatics University of Edinburgh Edinburgh United Kingdom Health Data Research UK United Kingdom

Emerging diseases present challenges in symptom recognition and timely clinical intervention due to limited available information. An effective prognostic model could assist physicians in making accurate diagnoses and designing personalized treatment plans to prevent adverse outcomes. However, in the early stages of disease emergence, several factors hamper model development: limited data collection, insufficient clinical experience, and privacy and ethical concerns restrict data availability and complicate accurate label assignment. Furthermore, Electronic Medical Record (EMR) data from different diseases or sources often exhibit significant cross-dataset feature misalignment, severely impacting the effectiveness of deep learning models. We present a domain-invariant representation learning method that constructs a transition model between source and target datasets. By constraining the distribution shift of features generated across different domains, we capture domain-invariant features specifically relevant to downstream tasks, developing a unified domain-invariant encoder that achieves better feature representation across various task domains. Experimental results across multiple target tasks demonstrate that our proposed model surpasses competing baseline methods and achieves faster training convergence, particularly when working with limited data. Extensive experiments validate our method’s effectiveness in providing more accurate predictions for emerging pandemics and other diseases. Code is publicly available at https://***/wang1yuhang/domain_invariant_network. Copyright © 2023, The Authors. All rights reserved.

关键词： Electronic health record

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Synthesising 3D Cardiac CINE-MR Images and Corresponding Segmentation Masks using a Latent Diffusion Model

Synthesising 3D Cardiac CINE-MR Images and Corresponding Seg...

引用

IEEE International Symposium on Biomedical Imaging

作者： Nina Cheng Zhengji Liu Yash Deo Haoran Dou Ning Bi Kun Wu Fengming Lin Zeike A Taylor Nishant Ravikumar Alejandro F Frangi CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Leeds School of Optometry The Hong Kong Polytechnic University NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We propose a novel pipeline for the generation of synthetic full spatial cine cardiac magnetic resonance (CMR) images via a latent Denoising Diffusion Implicit Models (DDIMs). These synthetic images can be used as viable alternatives to real data in deep learning model training for downstream cardiac image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic CMR images along with their corresponding segmentation masks. We evaluated model performance using a variety of methods, including generated image fidelity, diversity and calculated the volumes of the generated segmentation masks and compare it with the real segmentation masks. The proposed pipeline has the potential to be widely applied to other tasks in various medical imaging modalities. Effective and efficient generation of 3D cine cardiac images with corresponding segmentation masks can supplement real patient datasets and help reduce the burden of manually annotating images.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Biological system modeling Pipelines Noise reduction

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