# TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction

arXiv: https://arxiv.org/abs/2306.14406 · HTML: https://mlsanity.com/paper/2306.14406/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [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)
- [Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection](https://mlsanity.com/paper/2207.03482/) (2022)
- [Cross Language Image Matching for Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2203.02668/) (2022)
- [ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data](https://mlsanity.com/paper/2210.16467/) (2022)

## Built on by (most cited first, 11 of 11)

- [CLIP in Medical Imaging: A Survey](https://mlsanity.com/paper/2312.07353/) (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)
- [GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph](https://mlsanity.com/paper/2408.05502/) (2024)
- [RegFreeNet: A Registration-Free Network for CBCT-based 3D Dental Implant Planning](https://mlsanity.com/paper/2601.14703/) (2026)
- [Application Of Vision-Language Models For Assessing Osteoarthritis Disease Severity](https://mlsanity.com/paper/2401.06331/) (2024)
- [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 Condition Embedded Regression Network for Automated Dental Abutment Design](https://mlsanity.com/paper/2511.22578/) (2025)
- [Adaptive Texture-aware Masking for Self-Supervised Learning in 3D Dental CBCT Analysis](https://mlsanity.com/paper/2605.01741/) (2026)

## Abstract (arXiv)

When deep neural network has been proposed to assist the dentist in designing the location of dental implant, most of them are targeting simple cases where only one missing tooth is available. As a result, literature works do not work well when there are multiple missing teeth and easily generate false predictions when the teeth are sparsely distributed. In this paper, we are trying to integrate a weak supervision text, the target region, to the implant position regression network, to address above issues. We propose a text condition embedded implant position regression network (TCEIP), to embed the text condition into the encoder-decoder framework for improvement of the regression performance. A cross-modal interaction that consists of cross-modal attention (CMA) and knowledge alignment module (KAM) is proposed to facilitate the interaction between features of images and texts. The CMA module performs a cross-attention between the image feature and the text condition, and the KAM mitigates the knowledge gap between the image feature and the image encoder of the CLIP. Extensive experiments on a dental implant dataset through five-fold cross-validation demonstrated that the proposed TCEIP achieves superior performance than existing methods.