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A Two-Stage Semi-supervised nnU-Net Model for Automated Tooth Segmentation in Panoramic X-Ray images

A Two-Stage Semi-supervised nnU-Net Model for Automated Too...

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2nd international ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Ji, Changkai Liu, Yusheng He, Lanshan Jiang, Yuxian Huang, Chuanyi Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031889769

Automatic tooth segmentation in 2D panoramic X-ray images is crucial for various applications. Task 1 of the MICCAI STS 2024 Challenge aims to advance automated tooth segmentation techniques by providing datasets comprising labeled and unlabeled panoramic X-ray images. this paper addresses the challenge of limited labeled data by framing the problem as a semi-supervised learning task. We propose a two-stage deep learning model based on nnU-Net. the method first performs quadrant segmentation using one nnU-Net, followed by fine tooth segmentation using another nnU-Net within each quadrant. To effectively utilize unlabeled data, we implement a selective stability-based retraining strategy to generate reliable pseudo-labels. We further enhance model performance through post-processing methods such as connected domain analysis. Quantitative evaluation on the STS 2024 validation set demonstrates that our method achieves strong performance across several metrics (Dice_instance = 79.82%, Dice_image = 94.02%). In the competition’s validation phase, the method was awarded second place, validating its efficacy in automated dental segmentation of panoramic X-ray images. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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the Advancements of Autonomous Robotic Surgery: A Path Towards Fully Automated Procedures

The Advancements of Autonomous Robotic Surgery: A Path Towar...

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international conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Shivam Kumar Akancha Khaitan Ankush Lodh Pravin Kumar Samanta Amit Bakshi Bishnu Prasad De School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Electronics Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Autonomous robotic surgery stands at the leading edge of a revolution in healthcare where robots, artificial intelligence, machine learning, and advanced imaging promise to perform many surgical procedures with minimal direct human intervention. the potential of such systems is even in the poorest areas to augment access to professional care, to reduce recovery time, and increase precision. they are minimally invasive technologies which are designed to maximize treatment results with minimum errors. However, fully automated surgery is a challenge that requires adaptive decision-making, sophisticated sensory feedback, and the ability to overcome technical constraints. Adoption is further complicated by changing regulatory frameworks and ethical issues like patient consent and data protection. Economic constraints are caused by high development and implementation expenses. this study examines the potential for autonomous robotic surgery to revolutionize healthcare by reviewing recent research and clinical data. It presents the latest developments, key challenges, and potential future developments and, hence, provides an exhaustive overview of the merits and demerits of this technology in relation to changing the nature of surgical care.

关键词： Economics Ethics Minimally invasive surgery image edge detection Decision making Imaging Data protection medical services Machine learning Robot sensing systems

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Quantizing mobilenet models for classification problem 8

Quantizing mobilenet models for classification problem

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8th international conference on computing for Sustainable Global Development, INDIACom 2021

作者： Patel, Rupa Chaware, Anita S. N. D. T. Women's University Mumbai India Anna Leela College of Commerce and Economics Mumbai India

ISBN: (纸本)9789380544434

Mobile technology is evolving very rapidly. they have been integrated into the health care domain to assist health care professionals. the computer vision models are generally designed to be used for image detection and image classification problems. MobileNet is the computer vision model that performs well on the mobile devices but it requires cloud services for processing image information and providing the predictions to the users. this leads to increased latency. Processing image data on mobile devices will make the prediction faster. But mobiles are resource-restricted devices in terms of storage, power and computational speed. So an architecture that is small in size, efficient and has good prediction quality for image classification problems is required. Quantizing pre-trained MobileNet architecture compact the model representation and this reduces both the computational cost as well as memory requirement. In this paper quantization aware training approach is used for quantizing MobileNets architecture. Both quantized MobileNetvl and quantized MobileNetv2 models use integer computation only as against the original floating-point baseline models. this reduces the size of models and computational speed with the minimum difference in the accuracy. they are trained for classifying retinal fundus images according to the severity of diabetic retinopathy. From the experimental results, it is observed that the model size is compressed. the testing accuracy for MobileNetvl and MobileNetv2 model is 94% and 96% respectively while quantized MobileNetvl and quantized MobileNetv2 accuracy are observed to be 76% and 77% respectively. this novel approach can be used for classifying any medical image data on mobile devices. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： image classification

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Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate and Early Detection of Alzheimer’s and Parkinson’s Diseases 16th

Bagging the Best: A Hybrid SVM-KNN Ensemble for Accurate a...

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16th international conference on Brain Informatics, BI 2023

作者： Shaffi, Noushath Vimbi, Viswan Mahmud, Mufti Subramanian, Karthikeyan Hajamohideen, Faizal College of Computing and Information Sciences University of Technology and Applied Sciences-Sohar OM 311 Sohar Oman Department of Computer Science Nottingham Trent University NottinghamNG11 8NS United Kingdom CIRC and MTIF Nottingham Trent University NottinghamNG11 8NS United Kingdom

ISBN: (纸本)9783031430749

Deep Learning (DL) techniques have shown promise in the early detection of neurodegenerative diseases due to their ability to analyze large amounts of medical data accurately. However, their reliance on massive training data may not be ideal in the healthcare industry. therefore, this paper proposes a simple yet effective machine learning (ML) based hybrid ensemble of KNN and SVM for the early detection of Alzheimer’s Disease (AD) and Parkinson’s Disease (PD). the proposed method is hybrid in the sense that it combines the strengths of both non-parametric and parametric approaches, resulting in a more robust and accurate classification performance. the method is tested on two popular AD databases, ADNI and OASIS, and the NTUA PD dataset. the hybrid ensemble method achieves higher accuracy and specificity for AD and PD detection, which is on par with popular DL algorithms. the source code for this work can be accessed at https://***/snoushath/***. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Head and Neck Tumor Segmentation for MRI-Guided Radiation therapy Using Pre-trained STU-Net Models 1st

Head and Neck Tumor Segmentation for MRI-Guided Radiation Th...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th international conference on medical image computing and computer-assisted intervention, MICCAI 2024

作者： Wang, Zihao Lyu, Mengye College of Health Science and Environmental Engineering Shenzhen Technology University Shenzhen China College of Applied Sciences Shenzhen University Shenzhen China

ISBN: (纸本)9783031832734

Accurate segmentation of tumors in MRI-guided radiation therapy (RT) is crucial for effective treatment planning, particularly for complex malignancies such as head and neck cancer (HNC). this study presents a comparative analysis between two state-of-the-art deep learning models, nnU-Net v2 and STU-Net, for automatic tumor segmentation in pre-RT MRI images. While both models are designed for medical image segmentation, STU-Net introduces critical improvements in scalability and transferability, with parameter sizes ranging from 14 million to 1.4 billion. Leveraging large-scale pre-training on datasets such as TotalSegmentator, STU-Net captures complex and variable tumor structures more effectively. We modified the default nnU-Net v2 by adding additional convolutional layers to both the encoder and decoder, improving its performance for MRI data. Based on our experimental results, STU-Net demonstrated better performance than nnU-Net v2 in the head and neck tumor segmentation challenge. these findings suggest that integrating advanced models like STU-Net into clinical work- flows could remarkably enhance the precision of RT planning, poten- tially improving patient outcomes. Ultimately, the performance of the fine-tuned STU-Net-B model submitted for the final evaluation phase of Task 1 in this challenge achieved a DSCagg-GTVp of 0.76, a DSCagg- GTVn of 0.85, and an overall DSCagg-mean score of 0.81, securing ninth place in the Task 1 rankings. the described solution is by team SZTU- SingularMatrix for Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 challenge. Link to the trained model weights: https://***/Duskwang/Weight/releases. © the Author(s) 2025.

关键词： image segmentation

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Assessing Self-supervised xLSTM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications 1st

Assessing Self-supervised xLSTM-UNet Architectures for Head ...

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作者： Qayyum, Abdul Mazher, Moona Niederer, Steven A. National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom

ISBN: (纸本)9783031832734

Radiation therapy (RT) plays a pivotal role in treating head and neck cancer (HNC), with MRI-guided approaches offering superior soft tissue con- trast and daily adaptive capabilities that significantly enhance treatment preci- sion while minimizing side effects. To optimize MRI-guided adaptive RT for HNC, we propose a novel two-stage model for Head and Neck Tumor Segmenta- tion. In the first stage, we leverage a Self-Supervised 3D Student-Teacher Learn- ing Framework, specifically utilizing the DINOv2 architecture, to learn effec- tive representations from a limited unlabeled dataset. this approach effectively addresses the challenge posed by the scarcity of annotated data, enabling the model to generalize better in tumor identification and segmentation. In the second stage, we fine-tune an xLSTM-based UNet model that is specifically designed to capture both spatial and sequential features of tumor progression. this hybrid architecture improves segmentation accuracy by integrating temporal dependen- cies, making it particularly well-suited for MRI-guided adaptive RT planning in HNC. the model’s performance is rigorously evaluated on a diverse set of HNC cases, demonstrating significant improvements over state-of-the-art deep learning models in accurately segmenting tumor structures. Our proposed solu- tion achieved an impressive mean aggregated Dice Coefficient of 0.81 for pre- RT segments and 0.65 for mid-RT segments, underscoring its effectiveness in automated segmentation tasks. this work advances the field of HNC imaging by providing a robust, generalizable solution for automated Head and Neck Tumor Segmentation, ultimately enhancing the quality of care for patients undergo- ing RT. Our team name is DeepLearnAI (CEMRG). the code for this work is available at https://***/RespectKnowledge/SSL-based-DINOv2_Vision- LSTM_Head-and-Neck-Tumor_Segmentation. © the Author(s) 2025.

关键词： Self-supervised learning

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A Novel Approach for Repairing Unsegmented Liver Vascular images based on Centerline 23

A Novel Approach for Repairing Unsegmented Liver Vascular Im...

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Proceedings of the 2023 8th international conference on Biomedical Imaging, Signal Processing

作者： Lisha Zhao Zixuan Wang Kun Dai Quan Qi College of Information Science and Technology Shihezi University China Qingdao Hospital University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital) China AIMI Medical Technology Co. Ltd China

ISBN: (纸本)9798400716584

In liver vascular intervention surgery, the absence or narrowing vascular images may impact the understanding of patients’ vascular structure and morphology. Repairing and analyzing vascular images is a crucial task in computer-aided diagnosis and surgery for minimally invasive vascular diseases. Previous research has addressed the problem of repairing small areas with regular shapes or minor curvature changes in the holes, often leading to modifications to the existing points and encounter noise and geometric loss. Unlike existing point cloud completion algorithms, this paper aims to address the issue of missing vessels in liver vascular images that have not been segmented. It employs axis-aligned bounding boxes to extract missing vascular images. Based on the geometric characteristics of the liver vascular centerline, two methods of cross-section and spherical wave propagation are proposed for repair process. these methods take localized missing vascular images as input and preserve the complete topological structure of the vessels. the repair results are evaluated using the Chamfer distance and Dice similarity coefficient. Experimental results demonstrate that the proposed approach effectively repairs large-scale, non-closed missing vascular images in liver vascular images and generates complete vessel geometric models with accurate liver vascular structure and morphology. the validity of the vessel models is confirmed through hemodynamic analysis, providing robust support for medical image analysis and diagnosis.

关键词： computer-aided diagnosis image reconstruction image repair Vasculature

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VQ-ViCNet: Strengthen Unique Features Comparison Autoencoder with Embedding Space for Covid-19 image Classification

VQ-ViCNet: Strengthen Unique Features Comparison Autoencoder...

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IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Qide Liu Jielei Chu Hua Yu XinLei Wang Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China China E-port Data Center Chengdu Branch Chengdu China

ISBN: (纸本)9781665456579

In this paper, we propose a new novel coronavirus pneumonia image classification model based on the combination of Transformer and convolutional network(VQ-ViCNet), and present a vector quantization feature enhancement module for the inconspicuous characteristics of lung medical image data. this model extracts the local latent layer features of the image through the convolutional network, and learns the deep global features of the image data through the Transformer’s multi-head self attention algorithm. After the calculation of convolution and attention, the features learned by the Transformer Encoder are enhanced by the vector quantization feature enhancement module and able to better complete the final downstream tasks. this model performs better than convolutional architectures, pure attention architectures and generative models on all 6 public datasets.

关键词： COVID-19 Vector quantization Computational modeling Lung computer architecture Feature extraction Transformers

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Revolutionizing Alzheimer's Detection: Hybrid Deep Learning Models in Experimental Analysis and Insights

Revolutionizing Alzheimer's Detection: Hybrid Deep Learning ...

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international conference on Trends in Electronics and Informatics (ICEI)

作者： A. Praveena Y. Kalpana Department of Computer Science Vel's Institute of Technology & advanced Studies Chennai Tamil Nadu India Dept of Applied computing and Emerging Technologies Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

the detection and diagnosis of Alzheimer's Disease (AD) are critically important due to the increasing prevalence of the condition, especially among aging populations. this study investigates the enhancement of AD detection through hybrid deep learning models, utilizing a dataset from Kaggle comprising images categorized into four stages: Mild Demented, Very Mild Demented, Non-Demented, and Moderate Demented. We developed two innovative models: the FusionTree Classifier (FTC) and the FusionNet Integrator (FNI). the FTC combines Random Forest and Bagging techniques to reduce variance and overfitting, achieving an impressive accuracy of 96.78%. this hybrid approach leverages the strengths of ensemble learning methods, enhancing robustness and reliability in classification tasks. On the other hand, the FNI integrates VGG's powerful feature extraction capabilities with the classification strength of Support Vector Machines (SVMs), effectively identifying complex image patterns and achieving an even higher accuracy of 97.41 %. By combining convolutional neural networks (CNNs) for feature extraction with SVMs for classification, FNI capitalizes on the strengths of both techniques, demonstrating superior predictive accuracy compared to traditional methods. these advancements hold significant promise for improving diagnostic accuracy and patient outcomes in AD and other neurological conditions. the methodologies proposed in this study offer a reliable framework for early detection, which is crucial for timely intervention and management of the disease.

关键词： Support vector machines Deep learning Analytical models Accuracy Feature extraction Data models Alzheimer's disease Random forests Bagging Overfitting

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Cloud-based Data Analytics for Automated Coastal Cleanup Robots with Convolutional Neural Network

Cloud-based Data Analytics for Automated Coastal Cleanup Rob...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： A. Ramesh Babu Chethan Chandra S Basavaraddi Ramesh S S K Mouleeswaran Santosh Kumar Sahoo P. Solainayagi School of Computer Applications Vidya Vikas Institute of Engineering and Technology Mysore Karnataka India Department of Artificial Intelligence and Machine Learning R&D Don Bosco Institute of Technology Bangalore VTU Belagavi Karnataka India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Engineering and Technology VIT University Bhopal Madhya Pradesh India Department of Computer Science and Engineering Dayananda Sagar University Bangalore Karnataka India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

the growing danger of coastal pollution requires novel, automated remediation methods. this paper proposes combining autonomous robots with sophisticated sensor technologies and a Convolutional Neural Network (CNN)-based data analytics system to clean up coastal areas. the robots autonomously cruise coastal settings, discover and categories marine waste using onboard sensors, and send data to a cloud-based infrastructure for analysis. the proposed method relies on CNN, a deep learning model known for image recognition. For reliable identification and classification, CNN is trained on a broad collection of marine waste images. the cloud-based data analytics platform receives, processes, and stores cleaning robot data. It handles massive datasets effectively using cloud computing scalability and processing power. the technology uses real-time cloud image processing to identify garbage and enable robots to adjust their cleaning tactics. the data analytics platform generates detailed reports and visualizations to analyze cleaning efficiency and environmental effect. It advances automated coastal cleaning technology by integrating advanced robotics, sensor technologies, and deep learning. the proposed approach improves coastal cleaning speed, accuracy, and flexibility to protect marine habitats and biodiversity.

关键词： Deep learning Cloud computing Data analysis Pollution Accuracy Sea measurements Robot sensing systems Cleaning Real-time systems Convolutional neural networks

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