Intraoral Scans

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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Generative AI-Based Data Augmentation for Oral Lesion Classification: The PhotoMOCI Dataset and Benchmark
Sep 28, 2026Marco Parola, Mario G. C. A. Cimino, Sabrina SenatoreData Augmentation Intraoral Scans
Aalborg University, Denmark. · University of Pisa, Italy. · University of Salerno, Italy.
Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion
Sep 9, 2026Zhichun Jin, Zhicheng He, Hao Xu +4Intraoral Scans Occlusal Constraint
The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, China · State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases, Nanjing Medical University, Nanjing, China · Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing, China +1
APCReg: Anatomical-Prior-Guided Coarse-to-Fine CBCT--IOS Registration via Multi-View Projection and Reliability-Controlled Residual Correction
Aug 7, 2026Xincan Zheng, Yaqi Wang, Zhi Li +4Intraoral Scans Image Registration
School of Cyberspace Security, Hangzhou Dianzi University, Hangzhou, China · Innovation Center for Electronic Design Automation Technology, Hangzhou Dianzi University, Hangzhou, China · Department of Craniomaxillofacial Surgery, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China +2
Coarse-to-Fine Registration of Jawbone CT and Intraoral Scan Data Using GeDi and ICP with Pseudo-IOS Ground Truth
Aug 3, 2026Sho Mitarai, Hikaru Kayo, Hisashi Ozaki +2Intraoral Scans Image Registration
School of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan. · Department of Oral and Maxillofacial Surgery, Rakuwakai Otowa Hospital, Kyoto, Japan.
RobOralScan: Learning Active Intraoral Scanning for Robotic Dental Reconstruction
Jun 25, 2026Jinhyung Lee, Haeun Yun, Siwon Kim +4Intraoral Scans Occlusal Constraint
DGIST
Deep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images
Jun 4, 2026Jihun Cho, Soo-Yeon Jeong, Eun-Jeong Bae +1Intraoral Scans Occlusal Constraint
Dept. of Computer Engineering Pai Chai University Daejeon, Korea · Division of Software Engineering Pai Chai University Daejeon, Korea · Dept. of Dental Technology Bucheon University Bucheon, Korea
Pocket-Dentist: On-Device Dental Image Understanding via Efficient Multimodal Large Language Models
May 28, 2026Kai Bian, Xucheng Guo, Bin Chen +4Medical Vision-Language Models Intraoral Scans
The University of Auckland, New Zealand · Shandong University, China · The University of Melbourne, Australia
Intra-YOLO: A Small Object Detection Model for Caries and Molar-Incisor Hypomineralization in Intraoral Photography Based on Transfer Learning with Reinforcement Learning
May 27, 2026Po-Lun Chwang, Po-Yu Chang, Wen-Liang Lin +3Intraoral Scans Occlusal Constraint
Department of Mechanical Engineering, College of Engineering, National Yang Ming Chiao Tung University, Hsin-Chu, Taiwan · Taipei Medical University Hospital, Taipei, Taiwan · Wan Fang Hospital, Taipei Medical University, Taiwan
From Full and Partial Intraoral Scans to Crown Proposal: A Classification-Guided Restoration Assistance Pipeline
May 14, 2026Rabin Kunwar, Dikshya Parajuli, Rujal Acharya +12Intraoral Scans Occlusal Constraint
Accelerated Komputing Pvt. Ltd., Bhaktapur, Nepal. · University of Pittsburgh, Pittsburgh, PA, USA. · Emium Co. Ltd., Tokyo, Japan. +3
Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection
Apr 26, 2026Xuefen Liu, Xinquan Yang, Mianjie Zheng +5Intraoral Scans Rt-Detr
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. · School of Artificial Intelligence, Shenzhen University, Shenzhen, China. · Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China.
Dental Panoramic Radiograph Analysis Using YOLO26 From Tooth Detection to Disease Diagnosis
Apr 17, 2026Khawaja Azfar Asif, Rafaqat Alam KhanIntraoral Scans Yolo26
Department of Software Engineering, Lahore Garrison University, DHA Phase 6, Lahore, Pakistan
MetaDent: Labeling Clinical Images for Vision-Language Models in Dentistry
Apr 16, 2026Meng-Xun Li, Wen-Hui Deng, Zhi-Xing Wu +6Intraoral Scans Occlusal Constraint
Department of Prosthodontics, State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine, Ministry of Education, Hubei Key Laboratory of Stomatology, School and Hospital of Stomatology, Wuhan University, Wuhan, Hubei, China · School of Artificial Intelligence, Wuhan University, Wuhan, Hubei, China · School of Computer Science, Wuhan University, Wuhan, Hubei, China +2

Sep 28, 2026, cs.CVJ/K move · Enter open · S save

Generative AI-Based Data Augmentation for Oral Lesion Classification: The PhotoMOCI Dataset and Benchmark

Marco Parola, Mario G. C. A. Cimino, Sabrina Senatore

Aalborg University, Denmark. · University of Pisa, Italy. · University of Salerno, Italy.

Data Augmentation Intraoral Scans Synthetic Data

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Early detection of oral cancer via photographic imaging presents a promising avenue for large-scale oral cavity screening. However, the development of robust deep learning models is frequently hampered by the scarcity of high-quality, annotated datasets. To address this limitation, a novel and well-curated resource, the Photographic Multi-purpose Oral Cancer Imaging (PhotoMOCI) dataset, is introduced for developing models across multiple diagnostic tasks in oral oncology. Then, a comprehensive benchmark study was conducted to investigate how various data augmentation strategies influence the performance of image classifiers. Our analysis spans different generative AI frameworks, evaluating the efficacy of traditional methods against advanced generative approaches, including Generative Adversarial Networks (GANs) and Diffusion Models (DMs). Additionally, we propose the Synthetic Image Filter (SIF), a mechanism to select specific samples based on two auxiliary models: Synthetic Proxy Classifier to ensure samples are representative of the target class and Synthetic Image Detector to verify they appear realistic, thereby selecting only the high-utility images that contribute to improving downstream performance. Across the evaluated datasets and classifiers, the best SIF-filtered setup improves accuracy over traditional augmentation in all cases, with gains of +1.73% and +2.35% on PhotoMOCI and +2.38% and +2.08% on KOCD for ResNet50 and ViT, respectively. Our findings reveal that while the direct application of generative data augmentation may yield performance drops, the integration of SIF, considering (i) how synthetic data looks real and (ii) how it reflects the discriminative features of the belonging class, provides a simple yet effective mechanism to filter out synthetic samples that confuse the classifier during training.

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