# ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data arXiv: https://arxiv.org/abs/2210.16467 ยท HTML: https://mlsanity.com/paper/2210.16467/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 65 references that are in the map. - First posted on arXiv 29 Oct 2022. ## Builds on (most cited first, 28 of 28) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020) - [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020) - [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015) - [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013) - [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021) - [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020) - [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019) - [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017) - [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019) - [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021) - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022) - [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018) - [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019) - [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019) - [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020) - [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022) - [Conditional DETR for Fast Training Convergence](https://mlsanity.com/paper/2108.06152/) (2021) - [Early Convolutions Help Transformers See Better](https://mlsanity.com/paper/2106.14881/) (2021) - [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019) - [VarifocalNet: An IoU-aware Dense Object Detector](https://mlsanity.com/paper/2008.13367/) (2020) - [DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation](https://mlsanity.com/paper/2204.11911/) (2022) - [Training-Time-Friendly Network for Real-Time Object Detection](https://mlsanity.com/paper/1909.00700/) (2019) - [Circle Representation for Medical Object Detection](https://mlsanity.com/paper/2110.12093/) (2021) ## Built on by (most cited first, 11 of 11) - [A Recent Survey of Vision Transformers for Medical Image Segmentation](https://mlsanity.com/paper/2312.00634/) (2023) - [Two-Stream Regression Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2305.10044/) (2023) - [TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2306.14406/) (2023) - [TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2308.05355/) (2023) - [Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network](https://mlsanity.com/paper/2406.04603/) (2024) - [SSAD: Self-supervised Auxiliary Detection Framework for Panoramic X-ray based Dental Disease Diagnosis](https://mlsanity.com/paper/2406.13963/) (2024) - [RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning](https://mlsanity.com/paper/2601.14703/) (2026) - [SSA3D: Text-Conditioned Assisted Self-Supervised Framework for Automatic Dental Abutment Design](https://mlsanity.com/paper/2512.11507/) (2025) - [Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction](https://mlsanity.com/paper/2406.12186/) (2024) - [Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design](https://mlsanity.com/paper/2604.09047/) (2026) - [ImplantMamba: Long-range Sequential Modeling Mamba For Dental Implant Position Prediction](https://mlsanity.com/paper/2605.07082/) (2026) ## Abstract (arXiv) Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to automatically predict the implant position based on the oral CBCT data. We creatively propose to predict the implant position using the 2D axial view of the tooth crown area and fit a centerline of the implant to obtain the actual implant position at the tooth root. Convolutional stem and decoder are designed to coarsely extract image features before the operation of patch embedding and integrate multi-level feature maps for robust prediction, respectively. As both long-range relationship and local features are involved, our approach can better represent global information and achieves better location performance. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed ImplantFormer achieves superior performance than existing methods.