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Tooth segmentation is a pivotal step in modern digital dentistry, essential for applications across orthodontic diagnosis and treatment planning.
“Dental x-ray image segmentation”
Eyad Said, Gamal Fahmy, Diaa Nassar and Hany Ammar · 2004
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“Dental x-ray image segmentation”
Eyad Said, Gamal Fahmy, Diaa Nassar and Hany Ammar · 2004
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“Automatic segmentation of mandible in panoramic x-ray”
Amir Abdi, Shohreh Kasaei and Mojdeh Mehdizadeh · 2015
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“Automatic segmentation of mandible in panoramic x-ray”
Amir Abdi, Shohreh Kasaei and Mojdeh Mehdizadeh · 2015
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“3D U-Net: learning dense volumetric segmentation from sparse annotation”
Özgün Çiçek et al · 2016
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“3D U-Net: learning dense volumetric segmentation from sparse annotation”
Özgün Çiçek et al · 2016
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“Mask r-cnn”
Kaiming He, Georgia Gkioxari, Piotr Dollár and Ross Girshick · 2017
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“Grad-cam: Visual explanations from deep networks via gradient-based localization”
Ramprasaath Selvaraju et al · 2017
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“Attention is all you need”
Ashish Vaswani et al · 2017
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“Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets”
Lequan Yu et al · 2017
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“Mask r-cnn”
Kaiming He, Georgia Gkioxari, Piotr Dollár and Ross Girshick · 2017
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“Grad-cam: Visual explanations from deep networks via gradient-based localization”
Ramprasaath Selvaraju et al · 2017
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“Attention is all you need”
Ashish Vaswani et al · 2017
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“Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets”
Lequan Yu et al · 2017
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“Automatic multi-organ segmentation on abdominal CT with dense V-networks”
Eli Gibson et al · 2018
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“Attention u-net: Learning where to look for the pancreas. arXiv 2018”
Ozan Oktay et al · 2018
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“Automatic multi-organ segmentation on abdominal CT with dense V-networks”
Eli Gibson et al · 2018
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“Attention u-net: Learning where to look for the pancreas. arXiv 2018”
Ozan Oktay et al · 2018
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“ToothNet: automatic tooth instance segmentation and identification from cone beam CT images”
Zhiming Cui, Changjian Li and Wenping Wang · 2019
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“Rethinking the U-Net architecture for multimodal biomedical image segmentation”
M Ibtehaz Nabil · 2019
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“ToothNet: automatic tooth instance segmentation and identification from cone beam CT images”
Zhiming Cui, Changjian Li and Wenping Wang · 2019
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“Rethinking the U-Net architecture for multimodal biomedical image segmentation”
M Ibtehaz Nabil · 2019
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“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy et al · 2020
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“The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review”
Kuofeng Hung et al · 2020
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“A study on tooth segmentation and numbering using end-to-end deep neural networks”
Bernardo Silva, Laís Pinheiro, Luciano Oliveira and Matheus Pithon · 2020
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“A study on tooth segmentation and numbering using end-to-end deep neural networks”
Bernardo Silva, Laís Pinheiro, Luciano Oliveira and Matheus Pithon · 2020
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“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy et al · 2020
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“The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review”
Kuofeng Hung et al · 2020
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“A study on tooth segmentation and numbering using end-to-end deep neural networks”
Bernardo Silva, Laís Pinheiro, Luciano Oliveira and Matheus Pithon · 2020
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“A study on tooth segmentation and numbering using end-to-end deep neural networks”
Bernardo Silva, Laís Pinheiro, Luciano Oliveira and Matheus Pithon · 2020
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“Deep frequency re-calibration u-net for medical image segmentation”
Reza Azad et al · 2021
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“TSegNet: An efficient and accurate tooth segmentation network on 3D dental model”
Zhiming Cui et al · 2021
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“Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images”
Ali Hatamizadeh et al · 2021
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“A fully automated method for 3D individual tooth identification and segmentation in dental CBCT”
Tae Jang, Kang Kim, Hyun Cho and Jin Seo · 2021
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“Tufts dental database: a multimodal panoramic x-ray dataset for benchmarking diagnostic systems”
Karen Panetta et al · 2021
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“Transbts: Multimodal brain tumor segmentation using transformer”
Wang Wenxuan et al · 2021
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“Wavelet frequency separation attention network for chest x-ray image super-resolution”
Yue Yu, Kun She and Jinhua Liu · 2021
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“Deep frequency re-calibration u-net for medical image segmentation”
Reza Azad et al · 2021
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“TSegNet: An efficient and accurate tooth segmentation network on 3D dental model”
Zhiming Cui et al · 2021
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“Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images”
Ali Hatamizadeh et al · 2021
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“A fully automated method for 3D individual tooth identification and segmentation in dental CBCT”
Tae Jang, Kang Kim, Hyun Cho and Jin Seo · 2021
Earlier work this paper cites.
“Tufts dental database: a multimodal panoramic x-ray dataset for benchmarking diagnostic systems”
Karen Panetta et al · 2021
Earlier work this paper cites.
“Transbts: Multimodal brain tumor segmentation using transformer”
Wang Wenxuan et al · 2021
Earlier work this paper cites.
“Wavelet frequency separation attention network for chest x-ray image super-resolution”
Yue Yu, Kun She and Jinhua Liu · 2021
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“Unetr: Transformers for 3d medical image segmentation”
Ali Hatamizadeh et al · 2022
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“Current applications of deep learning and radiomics on CT and CBCT for maxillofacial diseases”
Kuo Hung et al · 2022
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Ho Lee, Shunxing Bao, Yuankai Huo and Bennett Landman · 2022
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“Tfcns: A cnn-transformer hybrid network for medical image segmentation”
Zihan Li et al · 2022
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“Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications”
Muhammad Maaz et al · 2022
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“S4nd: Modeling images and videos as multidimensional signals with state spaces”
Eric Nguyen et al · 2022
Cited alongside, same era.
“Unetr: Transformers for 3d medical image segmentation”
Ali Hatamizadeh et al · 2022
Cited alongside, same era.
“Current applications of deep learning and radiomics on CT and CBCT for maxillofacial diseases”
Kuo Hung et al · 2022
Cited alongside, same era.
Ho Lee, Shunxing Bao, Yuankai Huo and Bennett Landman · 2022
Cited alongside, same era.
“Tfcns: A cnn-transformer hybrid network for medical image segmentation”
Zihan Li et al · 2022
Cited alongside, same era.
“Edgenext: efficiently amalgamated cnn-transformer architecture for mobile vision applications”
“Dental Radiography Analysis and Diagnosis Dataset”
Mohamadreza Momeni · 2023
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“Tooth decay”
Ali Noranian · 2023
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“Tooth seg”
PranavKompally · 2023
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“Tooth Dataset”
Tabarka Rajab · 2023
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“Teeth segmentation in panoramic dental X-ray using mask regional convolutional neural network”
Giulia Rubiu et al · 2023
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“Teeth segmentation in panoramic dental X-ray using mask regional convolutional neural network”
Giulia Rubiu et al · 2023
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“Tooth Iteration4”
Shweta Shanbhag · 2023
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Muhammad Maaz et al · 2022
Cited alongside, same era.
“S4nd: Modeling images and videos as multidimensional signals with state spaces”
Eric Nguyen et al · 2022
Cited alongside, same era.
“StrongToothData”
Strick ailkes · 2023
Cited alongside, same era.
“ToothData”
Strick ailkes · 2023
Cited alongside, same era.
“Self-supervised learning with masked image modeling for teeth numbering, detection of dental restorations, and instance segmentation in dental panoramic radiographs”
Amani Almalki and Longin Latecki · 2023
Cited alongside, same era.
“CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation”
Zeyu Chen, Senyang Chen and Fengjun Hu · 2023
Cited alongside, same era.
“PaXNet: Tooth segmentation and dental caries detection in panoramic X-ray using ensemble transfer learning and capsule classifier”
Arman Haghanifar et al · 2023
Cited alongside, same era.
Later among the works it cites.
“Tooth detection”
Reem Shehab · 2023
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“Transformer-based deep learning network for tooth segmentation on panoramic radiographs”
Chen Sheng et al · 2023
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“Dental Panoramic Caries Segmentation Dataset”
Thunderpede · 2023
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“Toothdata”
wasdxa · 2023
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“CNN and swin-transformer based efficient model for Alzheimer’s disease diagnosis with sMRI”
Jiaming Xin et al · 2023
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“CoT-UNet++: A medical image segmentation method based on contextual Transformer and dense connection”
Yijun Yin, Wenzheng Xu, Lei Chen and Hao Wu · 2023
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“Simple parameter-free self-attention approximation”
Yuwen Zhai et al · 2023
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“Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection”
Yifan Zhang et al · 2023
Later among the works it cites.
“nnformer: Volumetric medical image segmentation via a 3d transformer”
Hong-Yu Zhou et al · 2023
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“Xnet: Wavelet-based low and high frequency fusion networks for fully-and semi-supervised semantic segmentation of biomedical images”
Yanfeng Zhou et al · 2023
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“Tooth2dSEG”
ZHYLAR · 2023
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“Matten: Video Generation with Mamba-Attention”
Yu Gao et al · 2024
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“UCFilTransNet: Cross-Filtering Transformer-based network for CT image segmentation”
Li Li et al · 2024
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“PointMamba: A Simple State Space Model for Point Cloud Analysis”
Dingkang Liang et al · 2024
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Jiacheng Ruan and Suncheng Xiang · 2024
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“Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation”
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