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检索条件"任意字段=2024 International Conference on Computing, Machine Learning and Data Science, CMLDS 2024"

共 13410 条记录，以下是161-170 订阅

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Registration of Longitudinal Liver Examinations for Tumor Progress Assessment

Registration of Longitudinal Liver Examinations for Tumor ...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on machine learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and Workshop on Multimodal learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

作者： Yassine, Walid Charachon, Martin Hudelot, Céline Ardon, Roberto MICS CentraleSupélec Université Paris Saclay Gif-sur-Yvette France Incepto Medical Paris France

ISBN: (纸本)9783031845246

Assessing cancer progression in liver CT scans is a clinical challenge, requiring a comparison of scans at different times for the same patient. Practitioners must identify existing tumors, compare them with prior exams, identify new tumors, and evaluate overall disease evolution. This process is particularly complex in liver examinations due to misalignment between exams caused by several factors. Indeed, longitudinal liver examinations can undergo different non-pathological and pathological changes due to non-rigid deformations, the appearance or disappearance of pathologies, and other variations. In such cases, existing registration approaches, mainly based on intrinsic features may distort tumor regions, biasing the tumor progress evaluation step and the corresponding diagnosis. This work proposes a registration method based only on geometrical and anatomical information from liver segmentation, aimed at aligning longitudinal liver images for aided diagnosis. The proposed method is trained and tested on longitudinal liver CT scans, with 317 patients for training and 53 for testing. Our experimental results support our claims by showing that our method is better than other registration techniques by providing a smoother deformation while preserving the tumor burden within the volume. Qualitative results emphasize the importance of smooth deformations in preserving tumor appearance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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A Federated learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation 9th

A Federated Learning-Friendly Approach for Parameter-Efficie...

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27th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Asokan, Mothilal Benjamin, Joseph Geo Yaqub, Mohammad Nandakumar, Karthik Mohamed bin Zayed Univ Artificial Intelligence MB Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031776090;9783031776106

Adapting foundation models for medical image analysis requires finetuning them on a considerable amount of data because of extreme distribution shifts between natural (source) data used for pre-training and medical (target) data. However, collecting task-specific medical data for such finetuning at a central location raises many privacy concerns. Although Federated learning (FL) provides an effective means for training on private decentralized data, communication costs in federating large foundation models can quickly become a significant bottleneck, impacting the solution's scalability. In this work, we address this problem of 'efficient communication while ensuring effective learning in FL' by combining the strengths of Parameter-Efficient Fine-tuning (PEFT) with FL. Specifically, we study plug-and-play Low-Rank Adapters (LoRA) in a federated manner to adapt the Segment Anything Model (SAM) for 3D medical image segmentation. Unlike prior works that utilize LoRA and finetune the entire decoder, we critically analyze the contribution of each granular component of SAM on finetuning performance. Thus, we identify specific layers to be federated that are very efficient in terms of communication cost while producing on-par accuracy. Our experiments show that retaining the parameters of the SAM model (including most of the decoder) in their original state during adaptation is beneficial because fine-tuning on small datasets tends to distort the inherent capabilities of the underlying foundation model. On Fed-KiTS, our approach decreases communication cost (similar to 48x.) compared to full fine-tuning while increasing performance (similar to 6%. Dice score) in 3D segmentation tasks. Our approach performs similar to SAMed while achieving similar to 2.8x reduction in communication and parameters to be finetuned. We further validate our approach with experiments on Fed-IXI and Prostate MRI datasets. Our code is available at https://***/BioMedIA-MBZUAI/FLAP-SAM.

关键词： Federated learning Foundation Model 3D Medical Image Segmentation Parameter-Efficient Fine-Tuning

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23rd IFIP TC 14 international conference on Entertainment computing, ICEC 2024

23rd IFIP TC 14 International Conference on Entertainment Co...

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23rd IFIP TC 14 international conference on Entertainment computing, ICEC 2024

ISBN: (纸本)9783031743528

The proceedings contain 38 papers. The special focus in this conference is on Entertainment computing. The topics include: Unveiling Computer Chess Evolution: Can machine learning Detect Historical Trends?;prototypical – A Framework for Board Game Creation;game Audio Description in Death of Internet: Insights from Players and Developers;ArtAI4DS: AI Art and Its Empowering Role in Digital Storytelling;Toward Scalable Content Generation for Gamified mHealth Interventions: The Evaluation of LLM-Generated Goals on User Engagement;the Effects of Roleplaying Orientations on Transformative learning and Perspective Taking in Virtual Reality;a Virtual Reality Simulator for Enhancing Marine Pollution Control Efforts;A Pattern-Oriented AI-Powered Approach to Story Composition;towards Game Design Elements on the Perception of a Fun Game: An Exergame Case Study;A Taxonomy of Collectible Card Games from a Game-Playing AI Perspective;Introducing Open-Sourced AI to Art and Design Education: A Gamified Course on LoRA Model Training;virtual Reality Visions from science Fiction Movies;accessible Heads-Up computing;automated Clustering of Video Games into Groups with Distinctive Names;Enhancing Orientation and Mobility Skills for Persons with Visual Impairments Through VR Games: Introducing the EdKit Unity Package;virtual Reality and Accessibility: What Users Need?;from Pixels to Feelings: Exploring Game Elements for Eliciting Empathy Toward Refugees;Harmony in Hierarchy: Mixed-Initiative Music Composition Inspired by WFC;DPGame: Game-Based learning for Dynamic Programming Algorithms;are Aesthetics and Accessibility Conflicting Aspects in Serious Games Design?;ethics of Mixed Reality Entertainment;challenges in data Visualization with Extended Reality Devices;game Accessibility Research Summit Workshop;Aesthetics and Empowerment: Exploring AI-Driven Creativity;dungeon Wipe: Exploring Dynamic Difficulty Adjustment with Power-Up Mechanics;answering Anya: A Virtual and Mixed Reality Game

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ABBA-VSM: Time Series Classification Using Symbolic Representation on the Edge 22nd

ABBA-VSM: Time Series Classification Using Symbolic Represen...

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22nd international conference on Service Oriented computing

作者： Kanatbekova, Meerzhan Ilager, Shashikant Brandic, Ivona TU Wien Vienna Austria

ISBN: (纸本)9789819608041;9789819608058

In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled and latency-sensitive services to application users using machine learning (ML) algorithms, e.g., Time Series Classification (TSC). However, existing TSC algorithms require access to full raw data and demand substantial computing resources to train and use them effectively in runtime. This makes them impractical for deployment in resource-constrained Edge environments. To address this, in this paper, we propose an Adaptive Brownian Bridge-based Symbolic Aggregation Vector Space Model (ABBA-VSM). It is a new TSC model designed for classification services on Edge. Here, we first adaptively compress the raw time series into symbolic representations, thus capturing the changing trends of data. Subsequently, we train the classification model directly on these symbols. ABBA-VSM reduces communication data between IoT and Edge devices, as well as computation cycles, in the development of resource-efficient TSC services on Edge. We evaluate our solution with extensive experiments using datasets from the UCR time series classification archive. The results demonstrate that the ABBA-VSM achieves up to 80% compression ratio and 90-100% accuracy for binary classification. Whereas, for non-binary classification, it achieves an average compression ratio of 60% and accuracy ranging from 60-80%.

关键词： Edge computing EdgeAI Time Series Classification data Compression Symbolic Representation

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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th international Workshop on machine learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive learning

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MultiAICL: Multi-task Tuning for Augmented In-Context learning in Text Style Transfer 13th

MultiAICL: Multi-task Tuning for Augmented In-Context Learni...

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13th international conference on Natural Language Processing and Chinese computing

作者： Zhu, Linan Zhou, Zehai Chen, Xiangfan Guo, Xiaolei Kong, Xiangjie Zhejiang Univ Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9789819794362;9789819794379

In-context learning (ICL) enhances the performance of large language models (LLMs) across various natural language process (NLP) tasks by simply demonstrating a few-shot of examples or instructions during inference. However, ICL still encounters significant challenges on the text style transfer (TST) tasks, which require high levels of model reasoning. The existing ICL ability has not been further developed because LLMs lack the process of training and learning in context. To address these issues, we introduce Multi-Task Tuning for Augmented In-Context learning (MultiAICL), a framework designed to enhance model ICL ability by simulating LLM's supervised fine-tuning steps. MultiAICL contains three main components: firstly, we construct example instructions for multiple tasks from the text corpus, where these examples are in the form of text-label pairs;secondly, we propose the Multi-Task Tuning (MTT) module, which tunes the model by randomly combining example instructions;and thirdly, we design the Augmented In-context learning (AICL) module, which incorporates different tasks into example templates to infer the model. MultiAICL improves the ICL ability of LLMs while maintaining their generalization across multiple tasks, thus encouraging models to generate high-quality text. Extensive experiments show that MultiAICL achieves excellent results on all 6 TST tasks, even outperforming larger LLMs. The code and data are available at https://***/fuz999/NLPCC-2024- MultiAICL.

关键词： In-Context learning Text Style Transfer Large Language Models

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A Case Study on Stunting Classification in Toddlers from Karyajaya South Sumatra Using Extreme Gradient Boosting and K-Nearest Neighbors

A Case Study on Stunting Classification in Toddlers from Kar...

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2025 international conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Abdurrahman, Raihan Palton Anuwiksa, I Wayan Gunawan, Putu Harry School of Computing Telkom University Bandung Indonesia

ISBN: (纸本)9798331513320

Stunting, a consequence of persistent malnutrition, severely hinders the physical and cognitive development of children under five years of age. In Indonesia, the incidence of stunting was 21.6% in 2022, presenting a significant public health challenge. This research seeks to create efficient machine learning (ML) models for identifying stunting status in chil-dren, utilizing a dataset of 3,000 physical measurements from the Karyajaya Health Center in South Sumatra. The study utilizes two classification algorithms: K-Nearest Neighbors (KNN) and Extreme Gradient Boosting (XGBoost). data preprocessing encompassed feature selection, outlier elimination via the Interquartile Range (IQR) approach, and rectification of class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE) and parameter optimization. The KNN model was set with five neighbors and distance-based weighting, resulting in an accuracy of 99.43% and an F1-score of 93.89%. The XGBoost model, optimized with a calibrated scale-pos-weight parameter, surpassed KNN by achieving an accuracy of 99.93% and an F1-score of 99.62%. These findings illustrate XGBoost's exceptional capacity to manage intricate, unbalanced datasets and underscore its promise as a reliable instrument for early stunting identification. The application of XGBoost can assist policymakers and healthcare professionals in delivering prompt interventions, therefore aiding the national objective of decreasing stunting prevalence to 14% by 2024. Subsequent study could improve model accuracy by integrating supplementary genetic and environmental variables across various demographic contexts. © 2025 IEEE.

关键词： Nearest neighbor search

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Enhanced Stroke Prediction Through Integrated Classification and Hybrid Sampling Techniques 3rd

Enhanced Stroke Prediction Through Integrated Classification...

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3rd international conference on machine learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Peng, Guangxi Liu, Jie Guangdong University of Science and Technology Dongguan China Baise University Baise China

ISBN: (纸本)9789819624676

This paper introduces a integrated machine learning model designed to predict stroke risk by analyzing various contributing factors. The model was rigorously trained and validated on an imbalanced dataset, encompassing numerous features associated with stroke risk. To enhance the model's performance, we applied hybrid sampling techniques to address data imbalance and used Grid Search to precisely identify the optimal parameters for our five foundational models. Among these, the Integrated Classification Method achieved remarkable precision, recall, accuracy, and F1-score, ranging from 94% to 95%. This high level of performance underscores the model's robustness and its potential to significantly aid clinical decision-making in stroke risk prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Multi-keyword Searchable data Auditing for Cloud-Based machine learning 6th

Multi-keyword Searchable Data Auditing for Cloud-Based Mach...

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6th international conference on machine learning for Cyber Security, ML4CS 2024

作者： Yu, Haiyan Ma, Qingru Zhu, Yilu Cui, Yuxin Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Sci-Tech University Hangzhou310018 China

ISBN: (纸本)9789819645657

machine learning (ML) relies on large amounts of data for training, which often requires outsourcing data to cloud servers. This raises concerns about data integrity and security. Preventing data from being tampered with or lost during storage and transmission has become a critical issue in the ML process. This paper presents a novel data auditing scheme for cloud-based ML, which supports multi-keyword search functionality. It allows searching one or more keywords to ensure the integrity of data in model training. To avoid data privacy leakage, the keywords are encrypted during the auditing process. Only the encrypted keywords of the data required for model training need to be provided to the third-party auditor (TPA). Subsequently, TPA can check and ensure the integrity of the relevant data. Through security analysis and performance evaluations, the results indicate that this scheme can effectively and reliably audit outsourced data while ensuring the security and privacy of model training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data integrity

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Intrusion Detection in Wireless Sensor Networks using machine learning 4

Intrusion Detection in Wireless Sensor Networks using Machin...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Fares, Hajar Vibhute, Amol D. Mouniane, Yassine Bouijij, Habiba Equipe of Telecommunication and Detection Faculty of Science Abdelmalek Essaadi University Tetouan Morocco MH Pune411016 India Natural Resources and Sustainable Development Laboratory Faculty of Sciences Ibn Tofail University B.P 242 Kenitra Morocco ENSIAS SSLAB Mohammed v University in Rabat Rabat Morocco

Attackers are continuously enhancing their techniques to attack confidential data. Thus, the classical security solutions based on cryptography and older detection systems must be revised, especially for a network dataset. Wireless sensor networks (WSN) consist of several smart devices distributed randomly in hostile areas with many restrictions, such as low operational time, short memory and minimal energy resources, and are susceptible to numerous attacks, such as Denial-of-Service (DoS). Recently, intrusion detection based on artificial intelligence models has provided an alternative and efficient solution with low computational resources. Therefore, the present study provides an innovative approach for detecting DoS attacks in the network using machine learning. Three machine learning methods, such as Support Vector machine (SVM), K-Nearest Neighbor (KNN) and decision tree, were implemented with a well-known WSN-DS data specialized for DoS attacks. The performance of the models has been evaluated using standard metrics like accuracy, precision, F1-score and recall. The experimental results demonstrated the effectiveness of the KNN model after achieving the highest accuracy of 99.76% in DoS detection. Thus, the present study's approach can be used in real-time intrusion detection. © 2025 The Authors. Published by Elsevier B.V.

关键词： Network intrusion

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