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共 2691 条记录，以下是711-720 订阅

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Orthopedic Walker Fall Detection and Motion Classification Using Bidirectional Gated Recurrent Unit Neural Network 10

Orthopedic Walker Fall Detection and Motion Classification U...

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10th international conference on Digital Arts, Media and Technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Hnoohom, Narit Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Accurate fall detection for orthopedic walker users is essential for timely medical intervention and enhancing safety for elderly and mobility-impaired individuals. this paper introduces a new method using a bidirectional gated recurrent unit (BiGRU) neural network to detect falls and classify motion patterns in walker usage. We use the Walker dataset, which includes inertial measurement unit sensor data for four motion classes: idle, motion, step, and fall. the dataset consists of 6-axis IMU readings (3-axis accelerometer and 3-axis gyroscope) from sensors mounted on orthopedic walkers, with around 620 samples per class. Our BiGRU model leverages temporal dependencies in both forward and backward directions to improve classification accuracy. We assess the model's performance in two scenarios: binary classification (fall vs. non-fall) and multi-class classification of all four motion types. the proposed architecture shows strong performance, achieving 99.79% accuracy and a 99.73% F1-score in binary classification, and 98.51% accuracy and a 98.50% F1-score in multi-class classification. It outperforms traditional deep learning models like CNN, LSTM, and GRU. the experimental results confirm the effectiveness of bidirectional processing in capturing temporal motion patterns, indicating significant potential for realtime fall detection in walker-assisted mobility. the high precision and recall values in both scenarios demonstrate the model's reliability for practical healthcare monitoring applications. © 2025 IEEE.

关键词： Recurrent neural networks

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Priori and Posteriori Attention for Generalizing Head and Neck Tumors Segmentation 1

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

作者： Lu, Jiangshan Lei, Wenhui Gu, Ran Wang, Guotai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

Head and neck cancer is one of the most common cancers in the world. the automatic segmentation of head and neck tumors with the help of computer is of great significance for treatment. In the context of the MICCAI 2021 HEad and neCK tumOR (HECKTOR) segmentation challenge, we propose a combination of a priori and a posteriori attention to segment tumor regions from PET/CT images. Specifically, 1) According to the imaging characteristics of PET, we use the normalized PET as an attention map to emphasize the tumor area on CT as a priori attention. 2) We add channel attention to the model as a posteriori attention. 3) For the test set contains unseen domains, we use Mixup to mix the PET and CT in the train set to simulate unseen domains and enhance the generalization of the network. Our results on the test set are produced with the use of an ensemble of multiple models, and our method ranked third place in the MICCAI 2021 HECKTOR challenge with DSC is 0.7735 and HD95 is 3.0882. © 2022, Springer Nature Switzerland AG.

关键词： image segmentation

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3rd international Workshop on Deep Learning in medical image Analysis, DLMIA 2017 and 7th international Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

3rd International Workshop on Deep Learning in Medical Image...

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3rd international Workshop on Deep Learning in medical image Analysis, DLMIA 2017 and 7th international Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319675572

the proceedings contain 43 papers. the special focus in this conference is on Deep Learning in medical image Analysis. the topics include: Simultaneous multiple surface segmentation using deep learning;a deep residual inception network for HEp-2 cell classification;joint segmentation of multiple thoracic organs in CT images with two collaborative deep architectures;accelerated magnetic resonance imaging by adversarial neural network;left atrium segmentation in CT volumes with fully convolutional networks;3d randomized connection network with graph-based inference;adversarial training and dilated convolutions for brain mri segmentation;CNNs enable accurate and fast segmentation of drusen in optical coherence tomography;domain-adversarial neural networks to address the appearance variability of histopathology images;accurate lung segmentation via network-wise training of convolutional networks;computationally efficient cardiac views projection using 3d convolutional neural networks;a deep level set method for image segmentation;context-based normalization of histological stains using deep convolutional features;transitioning between convolutional and fully connected layers in neural networks;pathological pulmonary lobe segmentation from CT images using progressive holistically nested neural networks and random walker;stain colour normalisation to improve mitosis detection on breast histology images;a unified framework for tumor proliferation score prediction in breast histopathology and generalised dice overlap as a deep learning loss function for highly unbalanced segmentations.

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8th international conference on Information, Communication and computing Technology, ICICCT 2023

8th International Conference on Information, Communication a...

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8th international conference on Information, Communication and computing Technology, ICICCT 2023

ISBN: (纸本)9783031438370

the proceedings contain 14 papers. the special focus in this conference is on international conference on Information, Communication and computing Technology. the topics include: High Speed and Efficient Reconfigurable Histogram Equalisation Architecture for image Contrast Enhancement;A Practical Use for AI-Generated images;homomorphic Encryption Schemes Using Nested Matrices;a Deep Learning Model for Heterogeneous Dataset Analysis - Application to Winter Wheat Crop Yield Prediction;An ARIMA and XGBoost Model Utilized for Forecasting Municipal Solid Waste Generation;an Empirical Study of Intrusion Detection by Combining Clustering and Classification Methods;personalized Movie Recommendation Prediction Using Reinforcement Learning;an Ensemble and Deep Neural Network Based Approaches for Automated Sentiment Analysis;In-store Product Placement Using LiDAR-assisted Discrete PSO Algorithm;deep Residual Variational Autoencoder for image Super-Resolution;Hybrid HAN Model to Investigate Depression from Twitter Posts;transfer Learning-Based Encoder-Decoder Model for Skin Lesion Segmentation.

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A Coarse-to-Fine Framework for Head and Neck Tumor Segmentation in CT and PET images 1

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作者： An, Chengyang Chen, Huai Wang, Lisheng Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

Radiomics analysis can help patients suffered from head and neck (H&N) cancer customize tailoring treatments. It requires a large number of segmentation of the H&N tumor area in PET and CT images. However, the cost of manual segmentation is extremely high. In this paper, we propose a coarse-to-fine framework to segment the H&N tumor automatically in FluoroDeoxyGlucose (FDG)-Positron Emission Tomography (PET) and Computed Tomography (CT) images. Specifically, we trained three 3D-UNets with residual blocks to make coarse stage, fine stage and refined stage predictions respectively. Experiments show that such a training framework can improve the segmentation quality step by step. We evaluated our framework with Dice Similarity Coefficient (DSC) and Hausdorff Distance at 95% (HD95) of 0.7733 and 3.0882 respectively in the task 1 of the HEad and neCK TumOR segmentation and outcome prediction in PET/CT images (HECKTOR2021) Challenge and ranked second. © 2022, Springer Nature Switzerland AG.

关键词： Positron emission tomography

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3rd international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2017 held in conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

3rd International Workshop on Patch-Based Techniques in Medi...

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3rd international Workshop on Patch-Based Techniques in medical Imaging, Patch-MI 2017 held in conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319674339

the proceedings contain 18 papers. the special focus in this conference is on Patch-Based Techniques in medical Imaging. the topics include: 4D multi-atlas label fusion using longitudinal images;brain image labeling using multi-atlas guided 3d fully convolutional networks;validation on ventriculomegaly patients;hippocampus subfield segmentation using a patch-based boosted ensemble of autocontext neural networks;on the role of patch spaces in patch-based label fusion;learning a sparse database for patch-based medical image segmentation;accurate and high throughput cell segmentation method for mouse brain nuclei using cascaded convolutional neural network;learning-based estimation of functional correlation tensors in white matter for early diagnosis of mild cognitive impairment;early prediction of Alzheimer’s disease with non-local patch-based longitudinal descriptors;adaptive fusion of texture-based grading: application to Alzheimer’s disease detection;micro-CT guided 3D reconstruction of histological images;a dictionary learning-based fast imaging method for ultrasound elastography;breast tumor detection in ultrasound images using deep learning;modeling the intra-class variability for liver lesion detection using a multi-class patch-based CNN;multiple sclerosis lesion segmentation using joint label fusion;deep multimodal case–based retrieval for large histopathology datasets and sparse representation using block decomposition for characterization of imaging patterns.

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Dual-Path Connected CNN for Tumor Segmentation of Combined PET-CT images and Application to Survival Risk Prediction 2nd

Dual-Path Connected CNN for Tumor Segmentation of Combined P...

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作者： Lee, Jiyeon Kang, Jimin Shin, Emily Yunha Kim, Regina E. Y. Lee, Minho Research Institute NEUROPHET Inc. Seoul06247 Korea Republic of

ISBN: (纸本)9783030982522

Automated segmentation methods for image segmentation have the potential to support diagnosis and prognosis using medical images in clinical practice. To achieve the goal of HEad and neCK tumOR (HECKTOR) segmentation and outcome survival prediction in PET/CT images in the MICCAI 2021 challenge, we proposed a novel framework to segment head and neck tumors by leveraging multi-modal imaging using a cross-attention module based on a dual-path and ensemble modeling with majority voting. In addition, we expanded our task to survival analysis using a random forest survival model to predict the prognosis of tumors using clinical information and segmented tumor volume. Our segmentation model achieved a Dice coefficient and Hausdorff distance of 0.7367 and 3.2700, respectively. Our survival model showed a concordance index (C-index) of 0.6459. © 2022, Springer Nature Switzerland AG.

关键词： image segmentation

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international Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017 and international Workshop on Point-of-Care Ultrasound: Algorithms, Hardware, and Applications, POCUS 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

International Workshop on Bio-Imaging and Visualization for ...

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international Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017 and international Workshop on Point-of-Care Ultrasound: Algorithms, Hardware, and Applications, POCUS 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319675510

the proceedings contain 19 papers. the special focus in this conference is on Bio-Imaging and Visualization for Patient-Customized Simulations. the topics include: Cortical envelope modeling for interactive patient-customized curvilinear reformatting in the native space;simulation of patient-specific deformable ultrasound imaging in real time;a 3D ultrasound informed model of the human gastrocnemius muscle;automatic liver lesion segmentation in CT combining fully convolutional networks and non-negative matrix factorization;constructing detailed subject-specific models of the human masseter;rapid prediction of personalised muscle mechanics: integration with diffusion tensor imaging;approaches to brain tissue quantification with comparison on supporting the detection of age-related dementia in MRI;relating atrial appendage flow stasis risk from computational fluid dynamics to imaging based appearance paradigms for cardioembolic risk;combining automated image analysis with obstetric sweeps for prenatal ultrasound imaging in developing countries;achieving fluid detection by exploiting shadow detection methods;a probe-camera system for 3D ultrasound image reconstruction;overall proficiency assessment in point-of-care ultrasound interventions and a novel ultrasound imaging method for 2D temperature monitoring of thermal ablation.

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1st international Workshop on Graphs in Biomedical image Analysis, GRAIL 2017, 6th international Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd international Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

1st International Workshop on Graphs in Biomedical Image Ana...

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1st international Workshop on Graphs in Biomedical image Analysis, GRAIL 2017, 6th international Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2017 and 3rd international Workshop on Imaging Genetics, MICGen 2017 held in Conjunction with 20th international conference on medical image computing and computer assisted intervention, MICCAI 2017

ISBN: (纸本)9783319676746

the proceedings contain 22 papers. the special focus in this conference is on Graphs in Biomedical image Analysis. the topics include: Classifying phenotypes based on the community structure of human brain networks;autism spectrum disorder diagnosis using sparse graph embedding of morphological brain networks;topology of surface displacement shape feature in subcortical structures;graph geodesics to find progressively similar skin lesion images;uncertainty estimation in vascular networks;extraction of airways with probabilistic state-space models and bayesian smoothing;detection and localization of landmarks in the lower extremities using an automatically learned conditional random field;bridge simulation and metric estimation on landmark manifolds;white matter fiber segmentation using functional varifolds;a new metric for statistical analysis of rigid transformations;unbiased diffeomorphic mapping of longitudinal data with simultaneous subject specific template estimation;exact function alignment under elastic riemannian metric;varifold-based matching of curves via sobolev-type riemannian metrics;computational anatomy in theano;rank constrained diffeomorphic density motion estimation for respiratory correlated computed tomography;efficient parallel transport in the group of diffeomorphisms via reduction to the lie algebra;multilevel modeling with structured penalties for classification from imaging genetics data and coupled dimensionality-reduction model for imaging genomics.

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Data Augmentation for Improving CNNs in medical image Classification

Data Augmentation for Improving CNNs in Medical Image Classi...

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6th international conference on Intelligent computing and Signal Processing (ICSP)

作者： Yi Ren Zengmin He Yang Deng Bo Huang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

medical image is essential for physicians to diagnose diseases. And convolutional neural networks (CNNs) have gained momentum for computer-aided diagnosis (CAD) medical image. However, there are still challenges in CNNs related to a lack of data and class-imbalanced datasets. Data augmentation is utilized to address the issues mentioned above. In this study, the Pseudo-color enhancement algorithm (OCEAN and TWILIGHT), common linear transform, CLAHE enhancement, and K-means clustering algorithm were applied to pneumonia X-ray images for data augmentation. thus, five processed datasets were finally obtained. the results indicated that five data-augmentation methods effectively improved the performance of CNNs for the pneumonia X-ray images binary classification detection, in which the CLAHE enhancement and OCEAN enhancement methods had the accuracy and F1 Score with 97.42%, 97.19%, and 97.42%, 98.36%, respectively. DenseNet121 showed the best classification performance of four CNNs utilized in this study, as evidenced by the area under the receiver operating characteristic (ROC) curves (AUC). To verify the generalizability of the two enhancement methods, CLAHE and OCEAN enhancement methods were applied to the Magnetic Resonance Imaging (MRI) dataset of Alzheimer's disease, which had the accuracy and F1 Score with 98.15% , 98.15%, and 97.22%, 97.68%, respectively. therefore, these simple and efficient data-augmentation methods effectively improve the performance of CNNs for diagnosing medical image, which can provide theoretical reference for physicians to diagnose the above two diseases, and advance the application of clinical medicine.

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