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检索条件"任意字段=5th International Conference on Medical Image Computing and Computer-Assisted Intervention"

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

the proceedings contain 102 papers. the special focus in this conference is on Cognitive computing and Cyber Physical Systems. the topics include: Comparative Analysis Between Fuzzy theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine Learning Models;defense Strategy Security Mechanism for Sensor Networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine Learning and image Processing;Leveraging the Power of MRMR in Machine Learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward Neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep Learning-Models for Kidney Tumor Segmentation using CT images;investigation of Quantum Machine Learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep Learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep Learning;Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using medical images;non-invasive Technique for Detecting Glycosuria through image Processing and Deep Learning Approaches;deep Learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep Learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;thz Wideband Metamaterial Absorber for Different Applications.

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Multi-IMU with Online Self-consistency for Freehand 3D Ultrasound Reconstruction 26th

Multi-IMU with Online Self-consistency for Freehand 3D Ultra...

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

作者： Luo, Mingyuan Yang, Xin Yan, Zhongnuo Li, Junyu Zhang, Yuanji Chen, Jiongquan Hu, Xindi Qian, Jikuan Cheng, Jun Ni, Dong Shenzhen Univ Sch Biomed Engn Hlth Sci Ctr Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med Ultrasound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China Shenzhen RayShape Med Technol Inc Shenzhen Peoples R China

ISBN: (纸本)9783031439063;9783031439070

Ultrasound (US) imaging is a popular tool in clinical diagnosis, offering safety, repeatability, and real-time capabilities. Freehand 3D US is a technique that provides a deeper understanding of scanned regions without increasing complexity. However, estimating elevation displacement and accumulation error remains challenging, making it difficult to infer the relative position using images alone. the addition of external lightweight sensors has been proposed to enhance reconstruction performance without adding complexity, which has been shown to be beneficial. We propose a novel online self-consistency network (OSCNet) using multiple inertial measurement units (IMUs) to improve reconstruction performance. OSCNet utilizes a modal-level self-supervised strategy to fuse multiple IMU information and reduce differences between reconstruction results obtained from each IMU data. Additionally, a sequence-level self-consistency strategy is proposed to improve the hierarchical consistency of prediction results among the scanning sequence and its sub-sequences. Experiments on large-scale arm and carotid datasets with multiple scanning tactics demonstrate that our OSCNet outperforms previous methods, achieving state-of-the-art reconstruction performance.

关键词： Multiple IMU Online Learning Freehand 3D Ultrasound

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Clustering Disease Trajectories in Contrastive Feature Space for Biomarker Proposal in Age-Related Macular Degeneration 26th

Clustering Disease Trajectories in Contrastive Feature Space...

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

作者： Holland, Robbie Leingang, Oliver Holmes, Christopher Anders, Philipp Kaye, Rebecca Riedl, Sophie Paetzold, Johannes C. Ezhov, Ivan Bogunovic, Hrvoje Schmidt-Erfurth, Ursula Scholl, Hendrik P. N. Sivaprasad, Sobha Lotery, Andrew J. Rueckert, Daniel Menten, Martin J. Imperial Coll London BioMedIA London England Med Univ Vienna Lab Ophthalm Image Anal Vienna Austria Moorfields Eye Hosp NHS Fdn Trust London England Inst Mol & Clin Ophthalmol Basel Basel Switzerland Univ Basel Dept Ophthalmol Basel Switzerland Univ Southampton Clin & Expt Sci Southampton Hants England Tech Univ Munich Munich Germany

ISBN: (纸本)9783031439896;9783031439902

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories that lack prognostic value for future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically propose biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. these are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system.

关键词： Contrastive learning Biomarker discovery Clustering Disease trajectories Age-related macular degeneration

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MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network for Renal Tumor Diagnosis 26th

MUVF-YOLOX: A Multi-modal Ultrasound Video Fusion Network fo...

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

作者： Li, Junyu Huang, Han Ni, Dong Xue, Wufeng Zhu, Dongmei Cheng, Jun Shenzhen Univ Sch Med Sch Biomed Engn Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China Shenzhen Univ Med UltraSound Image Comp MUSIC Lab Shenzhen Peoples R China Shenzhen Univ Marshall Lab Biomed Engn Shenzhen Peoples R China North Sichuan Med Coll Nanchong Cent Hosp Dept Ultrasound Nanchong Peoples R China

ISBN: (纸本)9783031439032;9783031439049

Early diagnosis of renal cancer can greatly improve the survival rate of patients. Contrast-enhanced ultrasound (CEUS) is a cost-effective and non-invasive imaging technique and has become more and more frequently used for renal tumor diagnosis. However, the classification of benign and malignant renal tumors can still be very challenging due to the highly heterogeneous appearance of cancer and imaging artifacts. Our aim is to detect and classify renal tumors by integrating B-mode and CEUS-mode ultrasound videos. To this end, we propose a novel multi-modal ultrasound video fusion network that can effectively perform multi-modal feature fusion and video classification for renal tumor diagnosis. the attention-based multi-modal fusion module uses cross-attention and self-attention to extract modality-invariant features and modality-specific features in parallel. In addition, we design an object-level temporal aggregation (OTA) module that can automatically filter low-quality features and efficiently integrate temporal information from multiple frames to improve the accuracy of tumor diagnosis. Experimental results on a multicenter dataset show that the proposed framework outperforms the single-modal models and the competing methods. Furthermore, our OTA module achieves higher classification accuracy than the frame-level predictions. Our code is available at https://***/JeunyuLi/MUAF.

关键词： Multi-modal Fusion Ultrasound Video Object Detection Renal Tumor

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Modeling Alzheimers' Disease Progression from Multi-task and Self-supervised Learning Perspective with Brain Networks 26th

Modeling Alzheimers' Disease Progression from Multi-task and...

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

作者： Liang, Wei Zhang, Kai Cao, Peng Zhao, Pengfei Liu, Xiaoli Yang, Jinzhu Zaiane, Osmar R. Northeastern Univ Comp Sci & Engn Shenyang Peoples R China Northeastern Univ Key Lab Intelligent Comp Med Image Minist Educ Shenyang Peoples R China Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang 110819 Peoples R China Nanjing Med Univ Affiliated Brain Hosp Nanjing Peoples R China DAMO Acad Alibaba Grp Hangzhou Peoples R China Univ Alberta Alberta Machine Intelligence Inst Edmonton AB Canada

ISBN: (纸本)9783031439063;9783031439070

Alzheimer's disease (AD) is a common irreversible neurodegenerative disease among elderlies. Establishing relationships between brain networks and cognitive scores plays a vital role in identifying the progression of AD. However, most of the previous works focus on a single time point, without modeling the disease progression with longitudinal brain networks data. Besides, the longitudinal data is insufficient for sufficiently modeling the predictive models. To address these issues, we propose a Self-supervised Multi-Task learning Progression model SMP-Net for modeling the relationship between longitudinal brain networks and cognitive scores. Specifically, the proposed model is trained in a self-supervised way by designing a masked graph auto-encoder and a temporal contrastive learning that simultaneously learn the structural and evolutional features from the longitudinal brain networks. Furthermore, we propose a temporal multi-task learning paradigm to model the relationship among multiple cognitive scores prediction tasks. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show the effectiveness of our method and achieve consistent improvements over state-of-the-art methods in terms of Mean Absolute Error (MAE), Pearson Correlation Coefficient (PCC) and Concordance Correlation Coefficient (CCC). Our code is available at https://***/IntelliDAL/Graph/tree/main/SMP-Net.

关键词： Self-supervised learning Multi-task learning Cognitive scores Brain networks Longitudinal prediction

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770807

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Shape-Based Pose Estimation for Automatic Standard Views of the Knee 26th

Shape-Based Pose Estimation for Automatic Standard Views of ...

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

作者： Kausch, Lisa thomas, Sarina Kunze, Holger El Barbari, Jan Siad Maier-Hein, Klaus H. German Canc Res Ctr Div Med Image Comp Heidelberg Germany Natl Ctr Tumor Dis NCT Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Univ Oslo Dept Informat Oslo Norway Siemens Healthineers Adv Therapy Syst Div Erlangen Germany MINTOS Res Grp Trauma Surg Clin Ludwigshafen Heidelberg Germany

ISBN: (纸本)9783031439896;9783031439902

Surgical treatment of complicated knee fractures is guided by real-time imaging using a mobile C-arm. Immediate and continuous control is achieved via 2D anatomy-specific standard views that correspond to a specific C-arm pose relative to the patient positioning, which is currently determined manually, following a trial-and-error approach at the cost of time and radiation dose. the characteristics of the standard views of the knee suggests that the shape information of individual bones could guide an automatic positioning procedure, reducing time and the amount of unnecessary radiation during C-arm positioning. To fully automate the C-arm positioning task during knee surgeries, we propose a complete framework that enables (1) automatic laterality and standard view classification and (2) automatic shape-based pose regression toward the desired standard view based on a single initial X-ray. A suitable shape representation is proposed to incorporate semantic information into the pose regression pipeline. the pipeline is designed to handle two distinct standard views with one architecture. Experiments were conducted to assess the performance of the proposed system on 3528 synthetic and 1386 real X-rays for the a.-p. and lateral standard. the view/laterality classificator resulted in an accuracy of 100%/98% on the simulated and 99%/98% on the real X-rays. the pose regression performance was d theta(a.- p) = 5.8 +/- 3.3 degrees, d theta(lateral) = 3.7 +/- 2.0 degrees on the simulated data and d theta(a.- p) = 7.4 +/- 5.0 degrees, d theta(lateral) = 8.4 +/- 5.4 degrees on the real data outperforming intensity-based pose regression.

关键词： Shape-based pose estimation Standard projections Knee

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Revealing Anatomical Structures in PET to Generate CT for Attenuation Correction 26th

Revealing Anatomical Structures in PET to Generate CT for At...

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

作者： Pan, Yongsheng Liu, Feihong Jiang, Caiwen Huang, Jiawei Xia, Yong Shen, Dinggang ShanghaiTech Univ Sch Biomed Engn Shanghai Peoples R China Northwest Univ Sch Informat Sci & Technol Xian Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Shanghai United Imaging Intelligence Co Ltd Shanghai Peoples R China

ISBN: (纸本)9783031439988;9783031439995

Positron emission tomography (PET) is a molecular imaging technique relying on a step, namely attenuation correction (AC), to correct radionuclide distribution based on pre-determined attenuation coefficients. Conventional AC techniques require additionally-acquired computed tomography (CT) or magnetic resonance (MR) images to calculate attenuation coefficients, which increases imaging expenses, time costs, or radiation hazards to patients, especially for whole-body scanners. In this paper, considering technological advances in acquiring more anatomical information in raw PET images, we propose to conduct attenuation correction to PET by itself. To achieve this, we design a deep learning based framework, namely anatomical skeleton-enhanced generation (ASEG), to generate pseudo CT images from non-attenuation corrected PET images for attenuation correction. Specifically, ASEG contains two sequential modules, i.e., a skeleton prediction module and a tissue rendering module. the former module first delineates anatomical skeleton and the latter module then renders tissue details. Both modules are trained collaboratively with specific anatomical-consistency constraint to guarantee tissue generation fidelity. Experiments on four public PET/CT datasets demonstrate that our ASEG outperforms existing methods by achieving better consistency of anatomical structures in generated CT images, which are further employed to conduct PET attenuation correction with better similarity to real ones. this work verifies the feasibility of generating pseudo CT from raw PET for attenuation correction without acquising additional images. the associated implementation is available at https://***/YongshengPan/ASEG-for-PET2CT.

关键词： PET Attenuation correction CT image generation

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medical image Classification Using Deep Learning for Brain Tumors Detection: An Overview 7th

Medical Image Classification Using Deep Learning for Brain T...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Alahmad, Hiba A. Al-Suhail, Ghaida A. Department of Computer Engineering University of Basrah Basrah Iraq

ISBN: (纸本)9783031733239

image analysis, including segmentation, detection and classification, has recently gained remarkable attention using deep learning. image classification plays a crucial role in computer-assisted diagnosis (CAD), but it poses a formidable challenge in image analysis. thus, deep learning can customize treatment plans based on patient data, genetics, and medical history. It excels at accurately detecting and classifying abnormalities in X-rays, MRIs, and CT scans, which facilitates early disease diagnosis by specialists. therefore, this paper firstly introduces an overview of image classification techniques for diagnosing a variety of human maladies and deep learning methods, such as autoencoders, ensemble learning, and transfer learning. Furthermore, the Convolutional Neural Network (CNN) to detect brain tumors in MRI images is also investigated to illustrate the profound effect of CNN in image classification. Experimental cases are conducted using the VGG-16 model for two datasets of different sizes. the results show that large dataset can achieve an accuracy of 99% at epoch 5 without data augmentation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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UNeXt: MLP-Based Rapid medical image Segmentation Network 25th

UNeXt: MLP-Based Rapid Medical Image Segmentation Network

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25th international conference on medical image computing and computer assisted intervention (MICCAI)

作者： Valanarasu, Jeya Maria Jose Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9783031164439;9783031164422

UNet and its latest extensions like TransUNet have been the leading medical image segmentation methods in recent years. However, these networks cannot be effectively adopted for rapid image segmentation in point-of-care applications as they are parameter-heavy, computationally complex and slow to use. To this end, we propose UNeXt which is a Convolutional multilayer perceptron (MLP) based network for image segmentation. We design UNeXt in an effective way with an early convolutional stage and a MLP stage in the latent stage. We propose a tokenized MLP block where we efficiently tokenize and project the convolutional features and use MLPs to model the representation. To further boost the performance, we propose shifting the channels of the inputs while feeding in to MLPs so as to focus on learning local dependencies. Using tokenized MLPs in latent space reduces the number of parameters and computational complexity while being able to result in a better representation to help segmentation. the network also consists of skip connections between various levels of encoder and decoder. We test UNeXt on multiple medical image segmentation datasets and show that we reduce the number of parameters by 72x, decrease the computational complexity by 68x, and improve the inference speed by 10x while also obtaining better segmentation performance over the state-of-the-art medical image segmentation architectures.

关键词： medical image segmentation MLP Point-of-care

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