Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking | Paper Citation Record

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This paper cites The impact of malocclusion and its treatment on quality of life: a literature review,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking The impact of malocclusion and its treatment on quality of life: a literature review,

Reference 1

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This paper cites Occlusion, malocclusion and method of measurements-an overview,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Occlusion, malocclusion and method of measurements-an overview,

Reference 2

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This paper cites Trends and application of artificial intelligence technology in orthodontic diagnosis and treatment plan- ning—a review,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Trends and application of artificial intelligence technology in orthodontic diagnosis and treatment plan- ning—a review,

Reference 3

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This paper cites The current status of cone beam computed tomography imaging in orthodontics,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking The current status of cone beam computed tomography imaging in orthodontics,

Reference 4

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This paper cites Deep learning for healthcare: review, opportunities and challenges,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Deep learning for healthcare: review, opportunities and challenges,

Reference 5

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This paper cites Spectral representation of behaviour primitives for depression analysis,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Spectral representation of behaviour primitives for depression analysis,

Reference 6

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This paper cites A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues,

Reference 7

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This paper cites Classification of dental diseases using cnn and transfer learning,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Classification of dental diseases using cnn and transfer learning,

Reference 8

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This paper cites Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm,

Reference 9

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This paper cites an unresolved cited work.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Unresolved cited work

Reference 10

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This paper cites Tooth detection and numbering in panoramic radiographs using convo- lutional neural networks,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Tooth detection and numbering in panoramic radiographs using convo- lutional neural networks,

Reference 11

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This paper cites Review of deep learning: concepts, cnn architectures, challenges, applications, future directions,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Review of deep learning: concepts, cnn architectures, challenges, applications, future directions,

Reference 12

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This paper cites Transformer-based deep learning network for tooth segmentation on panoramic radiographs,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Transformer-based deep learning network for tooth segmentation on panoramic radiographs,

Reference 13

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This paper cites Self-supervised learning with masked im- age modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Self-supervised learning with masked im- age modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs,

Reference 14

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This paper cites Self-Attention with Relative Position Representations.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Self-Attention with Relative Position Representations

Reference 15

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Observation c8ba2253-135a-4ff0-93fe-f185e9cbe595 · outbound

This paper cites Meshsnet: Deep multi-scale mesh feature learning for end-to-end tooth labeling on 3d dental surfaces,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Meshsnet: Deep multi-scale mesh feature learning for end-to-end tooth labeling on 3d dental surfaces,

Reference 16

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This paper cites Tucnet: A channel and spatial attention-based graph convolutional network for teeth upsampling and completion,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Tucnet: A channel and spatial attention-based graph convolutional network for teeth upsampling and completion,

Reference 17

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This paper cites A comprehensive survey on graph neural networks,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking A comprehensive survey on graph neural networks,

Reference 18

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This paper cites Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3d intraoral scanners,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3d intraoral scanners,

Reference 19

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This paper cites Deep instance segmentation of teeth in panoramic x-ray images,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Deep instance segmentation of teeth in panoramic x-ray images,

Reference 20

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This paper cites Dentnet: Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Dentnet: Deep neural transfer network for the detection of periodontal bone loss using panoramic dental radiographs,

Reference 21

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This paper cites Teeth detection and dental problem classification in panoramic x-ray images using deep learning and image processing techniques,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Teeth detection and dental problem classification in panoramic x-ray images using deep learning and image processing techniques,

Reference 22

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This paper cites Digital dental x-ray database for caries screening,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Digital dental x-ray database for caries screening,

Reference 23

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This paper cites Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection,

Reference 24

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This paper cites Diffusion-based hierarchical multi-label object detection to analyze panoramic dental x-rays,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Diffusion-based hierarchical multi-label object detection to analyze panoramic dental x-rays,

Reference 25

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This paper cites Multi-level uncertainty aware learning for semi-supervised dental panoramic caries segmentation,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Multi-level uncertainty aware learning for semi-supervised dental panoramic caries segmentation,

Reference 26

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This paper cites Automatic segmenting teeth in x-ray images: Trends, a novel data set, benchmarking and future perspectives,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Automatic segmenting teeth in x-ray images: Trends, a novel data set, benchmarking and future perspectives,

Reference 27

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 28

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Rethinking the inception architecture for computer vision,

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This paper cites Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs,

Reference 30

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This paper cites Utilizing mask r-cnn for detection and segmentation of oral diseases,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Utilizing mask r-cnn for detection and segmentation of oral diseases,

Reference 31

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Mask r-cnn,

Reference 32

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This paper cites Rdfnet: A fast caries detec- tion method incorporating transformer mechanism,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Rdfnet: A fast caries detec- tion method incorporating transformer mechanism,

Reference 33

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This paper cites Cavit: Early stage dental caries detection from smartphone-image using vision transformer,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Cavit: Early stage dental caries detection from smartphone-image using vision transformer,

Reference 34

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This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 35

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Swin transformer: Hierarchical vision transformer using shifted windows,

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This paper cites Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques

Reference 37

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking End-to-end object detection with transformers,

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Vision gnn: An image is worth graph of nodes,

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This paper cites The declaration of helsinki and public health,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking The declaration of helsinki and public health,

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Unresolved cited work

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Faster r-cnn: Towards real-time object detection with region proposal networks,

Reference 42

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Efficientdet: Scalable and efficient object detection,

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Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 45

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This paper cites GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Features

Reference 46

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This paper cites Learning multi- dimensional edge feature-based au relation graph for facial action unit recognition,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Learning multi- dimensional edge feature-based au relation graph for facial action unit recognition,

Reference 47

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This paper cites Imagenet: A large-scale hierarchical image database,.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking Imagenet: A large-scale hierarchical image database,

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This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking MMDetection: Open MMLab Detection Toolbox and Benchmark

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