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

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Abstract

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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant 82261138629; Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515010688 and 2021A1515220072; Shenzhen Municipal Science and Technology Innovation Council under Grant JCYJ2022053110141 2030 and JCYJ20220530155811025.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Xinquan Yang, Jinheng Xie, Xuechen Li & Linlin Shen

  2. AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen, China

    Xinquan Yang, Jinheng Xie, Xuechen Li & Linlin Shen

  3. National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China

    Xinquan Yang, Jinheng Xie, Xuechen Li & Linlin Shen

  4. Department of Stomatology, Shenzhen University General Hospital, Shenzhen, China

    Xuguang Li, Xin Li & Yongqiang Deng

  5. National University of Singapore, Singapore, Singapore

    Jinheng Xie

Authors
  1. Xinquan Yang

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  2. Jinheng Xie

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  3. Xuguang Li

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  4. Xuechen Li

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  5. Xin Li

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  6. Linlin Shen

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  7. Yongqiang Deng

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Corresponding author

Correspondence toLinlin Shen.

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Yang, X.et al. (2023). TCEIP: Text Condition Embedded Regression Network for Dental Implant Position Prediction. In: Greenspan, H.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14225. Springer, Cham. https://doi.org/10.1007/978-3-031-43987-2_31

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