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.

翻译：当深度神经网络被提出用于辅助牙医设计牙种植体位置时，大多数方法仅针对单颗牙齿缺失的简单病例。因此，现有方法在处理多颗牙齿缺失时效果不佳，且在牙齿稀疏分布时容易产生错误预测。本文尝试将弱监督文本（目标区域）整合到种植体位置回归网络中，以解决上述问题。我们提出一种文本条件嵌入的种植体位置回归网络（TCEIP），将文本条件嵌入编码器-解码器框架中以提升回归性能。同时提出一种由跨模态注意力（CMA）和知识对齐模块（KAM）构成的跨模态交互机制，用于促进图像与文本特征间的交互。其中CMA模块执行图像特征与文本条件之间的交叉注意力操作，而KAM模块则缩小图像特征与CLIP图像编码器之间的知识差距。在牙种植体数据集上采用五折交叉验证的广泛实验表明，所提出的TCEIP方法性能优于现有方法。