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

TL;DR

This paper introduces TCEIP, a novel neural network that integrates text conditions with image features to improve dental implant position prediction, especially in complex cases with multiple missing teeth.

Contribution

The paper proposes a new text-embedded regression network with cross-modal attention and knowledge alignment modules for enhanced accuracy in dental implant positioning.

Findings

TCEIP outperforms existing methods in accuracy.

The model effectively handles multiple missing teeth cases.

Cross-modal interaction improves feature relevance.

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.