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检索条件"任意字段=3rd International Conference on Medical Image Computing and Computer-Assisted Intervention"

共 4110 条记录，以下是61-70 订阅

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Quantifying Knee Cartilage Shape and Lesion: From image to Metrics 3rd

Quantifying Knee Cartilage Shape and Lesion: From Image to...

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3rd international Workshop on Applications of medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th international conference on medical image computing and computer assisted intervention, MICCAI 2024

作者： Yao, Yongcheng Chen, Weitian UKRI CDT in Biomedical AI School of Informatics The University of Edinburgh Edinburgh United Kingdom Department of Imaging and Interventional Radiology The Chinese University of Hong Kong China

ISBN: (纸本)9783031820069

Imaging features of knee articular cartilage have been shown to be potential imaging biomarkers for knee osteoarthritis. Despite recent methodological advancements in image analysis techniques like image segmentation, registration, and domain-specific image computing algorithms, only a few works focus on building fully automated pipelines for imaging feature extraction. In this study, we developed a deep-learning-based medical image analysis application for knee cartilage morphometrics, CartiMorph Toolbox (CMT). We proposed a 2-stage joint template learning and registration network, CMT-reg. We trained the model using the OAI-ZIB dataset and assessed its performance in template-to-image registration. The CMT-reg demonstrated competitive results compared to other state-of-the-art models. We integrated the proposed model into an automated pipeline for the quantification of cartilage shape and lesion (full-thickness cartilage loss, specifically). The toolbox provides a comprehensive, user-friendly solution for medical image analysis and data visualization. The software and models are available at https://***/YongchengYAO/CMT-AMAI24paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cartilage

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Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models 3rd

Head CT Scan Motion Artifact Correction via Diffusion-Based ...

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作者： Chen, Zhennong Yoon, Siyeop Strotzer, Quirin Khalid, Rehab Naeem Tivnan, Matthew Li, Quanzheng Gupta, Rajiv Wu, Dufan Center for Advanced Medical Computing and Analysis Massachusetts General Hospital and Harvard Medical School BostonMA02114 United States Department of Radiology Massachusetts General Hospital and Harvard Medical School BostonMA02114 United States

ISBN: (纸本)9783031820069

Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. image-domain-based motion correction is practical for routine use since it doesn’t rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts. Motivated by the improved image quality and more stable training of diffusion-based generative models, we propose a novel 3D head CT motion correction approach based on conditional diffusion, named HeadMotion-EDM (HM-EDM). This approach has three features. Firstly, we utilize motion-corrupted images as the conditional input. Secondly, we leverage the advanced Elucidated Diffusion Model (EDM) framework, which integrates several pivotal engineering improvements in the diffusion model and significantly expedites the sampling process. Thirdly, we design an efficient 3D-patch-wise training method for 3D CT data. Comparative studies demonstrate that our approach surpasses CNN-based techniques as well as the denoising diffusion probabilistic model (DDPM) in both simulation and phantom studies. Furthermore, radiologists reviewed the results of applying HM-EDM to real-world portable head CT scans, showing its effectiveness in eliminating motion artifacts and improving diagnostic value. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Segmentation Style Discovery: Application to Skin Lesion images 9th

Segmentation Style Discovery: Application to Skin Lesion Ima...

引用

27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Abhishek, Kumar Kawahara, Jeremy Hamarneh, Ghassan Simon Fraser Univ Sch Comp Sci Burnaby BC Canada AIP Labs Budapest Hungary

ISBN: (纸本)9783031776090;9783031776106

Variability in medical image segmentation, arising from annotator preferences, expertise, and their choice of tools, has been well documented. While the majority of multi-annotator segmentation approaches focus on modeling annotator-specific preferences, they require annotator-segmentation correspondence. In this work, we introduce the problem of segmentation style discovery, and propose StyleSeg, a segmentation method that learns plausible, diverse, and semantically consistent segmentation styles from a corpus of image-mask pairs without any knowledge of annotator correspondence. StyleSeg consistently outperforms competing methods on four publicly available skin lesion segmentation (SLS) datasets. We also curate ISIC-MultiAnnot, the largest multi-annotator SLS dataset with annotator correspondence, and our results show a strong alignment, using our newly proposed measure AS(2), between the predicted styles and annotator preferences. The code and the dataset are available at https://***/sfu-mial/StyleSeg.

关键词： inter-rater variability image segmentation dermatology

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Intelligent computing System for Breast Cancer Identification utilizing U-Net for Digital image Segmentation 3

Intelligent Computing System for Breast Cancer Identificatio...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Goli, Ranadheer Reddy Jadala, Vijaya Chandra Jamalpur, Bhavana Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep Learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This Intelligent computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis. © 2025 IEEE.

关键词： Breast Cancer Intelligent System Segmentation U-Net

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Advancements and Challenges in Quantum Machine Learning for medical image Classification: A Comprehensive Review 3

Advancements and Challenges in Quantum Machine Learning for ...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi World University of Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Quantum technologies are rapidly advancing as image classification tasks grow more complex due to large image volumes and extensive parameter updates required by traditional machine learning models. Quantum Machine Learning (QML) offers a promising solution for medical image classification. The parallelization of quantum computing can significantly improve speed and accuracy in disease detection and diagnosis. This paper provides an overview of recent studies on medical image classification through a structured taxonomy, highlighting key contributions, limitations and gaps in current research. It emphasizes moving from simulations to real quantum computers, addressing challenges like noisy qubits and suggests future research to enhance medical image classification using quantum technology. © 2025 IEEE.

关键词： Qubits

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FedGS: Federated Gradient Scaling for Heterogeneous medical image Segmentation 9th

FedGS: Federated Gradient Scaling for Heterogeneous Medical ...

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27th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Schutte, Philip Corbetta, Valentina Beets-Tan, Regina Silva, Wilson Univ Amsterdam Amsterdam Netherlands Netherlands Canc Inst Dept Radiol Amsterdam Netherlands Univ Utrecht Dept Informat & Comp Sci Dept Biol AI Technol Life Utrecht Netherlands Maastricht Univ GROW Sch Oncol & Dev Biol Med Ctr Maastricht Netherlands

ISBN: (纸本)9783031776090;9783031776106

Federated Learning (FL) in Deep Learning (DL)-automated medical image segmentation helps preserving privacy by enabling collaborative model training without sharing patient data. However, FL faces challenges with data heterogeneity among institutions, leading to suboptimal global models. Integrating Disentangled Representation Learning (DRL) in FL can enhance robustness by separating data into distinct representations. Existing DRL methods assume heterogeneity lies solely in style features, overlooking content-based variability like lesion size and shape. We propose FedGS, a novel FL aggregation method, to improve segmentation performance on small, under-represented targets while maintaining overall efficacy. FedGS demonstrates superior performance over FedAvg, particularly for small lesions, across PolypGen and LiTS datasets. The code and pre-trained checkpoints are available at the following link: https://***/Trustworthy-AI-UU-NKI/Federated-Learning-Disentanglement.

关键词： Federated learning

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DAST: Differentiable Architecture Search with Transformer for 3D medical image Segmentation 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Yang, Dong Xu, Ziyue He, Yufan Nath, Vishwesh Li, Wenqi Myronenko, Andriy Hatamizadeh, Ali Zhao, Can Roth, Holger R. Xu, Daguang NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

Neural Architecture Search (NAS) has been widely used for medical image segmentation by improving both model performance and computational efficiency. Recently, the Visual Transformer (ViT) model has achieved significant success in computer vision tasks. Leveraging these two innovations, we propose a novel NAS algorithm, DAST, to optimize neural network models with transformers for 3D medical image segmentation. The proposed algorithm is able to search the global structure and local operations of the architecture with a GPU memory consumption constraint. The resulting architectures reveal an effective relationship between convolution and transformer layers in segmentation models. Moreover, we validate the proposed algorithm on large-scale medical image segmentation data sets, showing its superior performance over the baselines. The model achieves state-of-the-art performance in the public challenge of kidney CT segmentation (KiTS'19).

关键词： Neural architecture search Transformer Segmentation

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The Role of Subgroup Separability in Group-Fair medical image Classification 1

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26th international conference on medical image computing and computer-assisted intervention (MICCAI)

作者： Jones, Charles Roschewitz, Melanie Glocker, Ben Imperial Coll London Dept Comp London England

ISBN: (数字)9783031438981

ISBN: (纸本)9783031438974;9783031438981

We investigate performance disparities in deep classifiers. We find that the ability of classifiers to separate individuals into subgroups varies substantially across medical imaging modalities and protected characteristics;crucially, we show that this property is predictive of algorithmic bias. Through theoretical analysis and extensive empirical evaluation (Code is available at https://***/biomedia-mira/subgroup-separability), we find a relationship between subgroup separability, subgroup disparities, and performance degradation when models are trained on data with systematic bias such as underdiagnosis. Our findings shed new light on the question of how models become biased, providing important insights for the development of fair medical imaging AI.

关键词： medical imaging

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A Comparative Analysis of Pancreatic Tumor Detection using VGG16, ResNet, and DenseNet 3

A Comparative Analysis of Pancreatic Tumor Detection using V...

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3rd international conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Deepthi, Gannamani Anusha Bamini, A.M. Praveen, Yenumala Joseph Karunya Institute of Technology and Sciences Division of Computer Science and Engineering India

ISBN: (纸本)9798350375190

This study explores the transformative potential of image classification algorithms like VGG16, ResNet, and DenseNet, for the early detection of pancreatic tumors using medical imaging. One of the main causes of cancer-related deaths globally is pancreatic cancer. However, pancreatic cancer detected at early stages can be cured. This study navigates through the evolution of CNNs in medical image analysis and their specific applications in pancreatic cancer detection. Pancreatic cancer poses a significant global health challenge due to its high mortality rates, often attributed to late-stage detection. The primary objective is to enhance diagnostic accuracy and enable timely intervention. The obtained results demonstrated promising accuracy levels for each model. VGG16 achieved an accuracy of 96%, ResNet demonstrated an accuracy of 98.31%, and DenseNet showcased an accuracy of 99%. Precision values for VGG16, ResNet, and DenseNet were 95%, 96%, and 95.2%, respectively. Recall values were 98% for VGG16, 95% for ResNet, and 97% for DenseNet. The F1 score for 98% for VGG16, 97% for ResNet, and 98.1% for DenseNet. The study concludes that VGG16, ResNet, and DenseNet are valuable tools for pancreatic tumor detection, with each architecture exhibiting unique advantages. By aiding in the early detection of pancreatic cancer, the suggested models may enhance patient outcomes. The research provides insights into the comparative performance of these deep learning architectures, guiding future developments in medical image analysis. © 2024 IEEE.

关键词： image classification

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1st BONBID-HIE Lesion Segmentation Challenge and 1st Trauma Thompson Challenge Held in Conjunction with 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

1st BONBID-HIE Lesion Segmentation Challenge and 1st Trauma ...

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1st BONBID-HIE Lesion Segmentation Challenge and 1st Trauma Thompson Challenge Held in Conjunction with 26th international conference on medical image computing and computer assisted intervention, MICCAI 2023

ISBN: (纸本)9783031716256

The proceedings contain 10 papers. The special focus in this conference is on BONBID-HIE Lesion Segmentation Challenge and Trauma Thompson Challenge. The topics include: Enhancing Lesion Segmentation in the BONBID-HIE Challenge: An Ensemble Strategy;An Ensemble Approach for Segmentation of Neonatal HIE Lesions;Improving Segmentation of Hypoxic Ischemic Encephalopathy Lesions by Heavy Data Augmentation: Contribution to the BONBID Challenge;a Deep Neural Network Approach for the Lesion Segmentation from Neonatal Brain Magnetic Resonance Imaging;SegResNet Based Reciprocal Transformation for BONBID-HIE Lesion Segmentation;Overview of the Trauma THOMPSON Challenge at MICCAI 2023;The Trauma THOMPSON Challenge Report MICCAI 2023;Action Recognition and Action Anticipation Tasks in the Trauma THOMPSON Challenge Technical Report;QuIIL at T3 Challenge: Towards Automation in Life-Saving intervention Procedures from First-Person View.

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