Dental Imaging

Latest papers

38 papers1 paper in the last four weeks, down 75% on the four weeks before. 0.0% of all new papers.

MATHENA: Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy
Apr 1, 2026Kyeonghun Kim, Jaehyung Park, Youngung Han +17Dental Imaging Medical Imaging
OUTTA, Seoul, South Korea · Seoul National University, Seoul, South Korea · Sangmyung University, Seoul, South Korea +4
DinoDental: Benchmarking DINOv3 as a Unified Vision Encoder for Dental Image Analysis
Mar 30, 2026Kun Tang, Xinquan Yang, Mianjie Zheng +6Medical Image Analysis Vision Foundation Model Adaptation
Shenzhen University. · Shenzhen University General Hospital. · Chongqing University.
TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
Mar 20, 2026Shaojie Zhuang, Lu Yin, Guangshun Wei +3Medical Imaging Dental Imaging
Shandong University, Jinan 250010, China · University of Surrey, Guildford, Surrey, United Kingdom · King’s College London, London, United Kingdom
IOSVLM: A 3D Vision-Language Model for Unified Dental Diagnosis from Intraoral Scans
Mar 17, 2026Huimin Xiong, Zijie Meng, Tianxiang Hu +33D VLMs Medical VLMs
ZJU-UIUC Institute, Zhejiang University, Haining, 314400, China · Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, 310058, China · Angelalign Research Institute, Angel Align Inc., Shanghai, 200011, China
Prompt-Based Caption Generation for Single-Tooth Dental Images Using Vision-Language Models
Mar 8, 2026Anastasiia Sukhanova, Aiden Taylor, Julian Myers +5Dental Imaging Image Captioning
Marshall University · West Virginia State University
OralGPT-Plus: Learning to Use Visual Tools via Reinforcement Learning for Panoramic X-ray Analysis
Mar 6, 2026Yuxuan Fan, Jing Hao, Hong Chen +5Medical Image Analysis Medical VLMs
The Hong Kong University of Science and Technology (GZ) · School of Computer Science, Peking University · Faculty of Dentistry, The University of Hong Kong +3
OPGAgent: An Agent for Auditable Dental Panoramic X-ray Interpretation
Feb 28, 2026Zhaolin Yu, Litao Yang, Ben Babicka +7Dental Imaging Medical Imaging
AIM for Health Lab, Faculty of Information Technology, Monash University, Melbourne, Australia · Faculty of Information Technology, Monash University, Melbourne, Australia · Curae Health, Melbourne, Australia +4
PanoDiff-SR: Synthesizing Dental Panoramic Radiographs using Diffusion and Super-resolution
Jul 12, 2025Sanyam Jain, Bruna Neves de Freitas, Andreas Basse-OConnor +2Medical Image Synthesis Medical Imaging
Department of Dentistry and Oral Health, Aarhus University, 8000 Aarhus, Denmark · Aarhus Institute of Advanced Studies, Aarhus University, 8000 Aarhus, Denmark · Department of Mathematics, Aarhus University, 8000 Aarhus, Denmark +1

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MATHENA: Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy

Kyeonghun Kim, Jaehyung Park, Youngung Han, Anna Jung+16

OUTTA, Seoul, South Korea · Seoul National University, Seoul, South Korea · Sangmyung University, Seoul, South Korea+4

Dental Imaging Medical Imaging Medical Image Segmentation Medical Image Anomaly Detection Object Detection State Space Models

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Dental diagnosis from Orthopantomograms (OPGs) requires coordination of tooth detection, caries segmentation (CarSeg), anomaly detection (AD), and dental developmental staging (DDS). We propose Mamba-based Architectural Tooth Hierarchical Estimator and Holistic Evaluation Network for Anatomy (MATHENA), a unified framework leveraging Mamba's linear-complexity State Space Models (SSM) to address all four tasks. MATHENA integrates MATHE, a multi-resolution SSM-driven detector with four-directional Vision State Space (VSS) blocks for O(N) global context modeling, generating per-tooth crops. These crops are processed by HENA, a lightweight Mamba-UNet with a triple-head architecture and Global Context State Token (GCST). In the triple-head architecture, CarSeg is first trained as an upstream task to establish shared representations, which are then frozen and reused for downstream AD fine-tuning and DDS classification via linear probing, enabling stable, efficient learning. We also curate PARTHENON, a benchmark comprising 15,062 annotated instances from ten datasets. MATHENA achieves 93.78% mAP@50 in tooth detection, 90.11% Dice for CarSeg, 88.35% for AD, and 72.40% ACC for DDS.