Abstract
We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries. To protect patients privacy, we design a software framework using image inpainting, which does not require cleft lip images for training, thereby mitigating the risk of model leakage. We implement a novel multi-task architecture that predicts both the non-cleft facial image and facial landmarks, resulting in better performance as evaluated by surgeons. The software is implemented with PyTorch and is usable with consumer-level color images with a fast prediction speed, enabling effective deployment.
Abstract (translated)
我们呈现了一种软件，用于预测患者唇裂术后的无唇裂面部图像，从而方便理解、认识和讨论唇裂术后治疗。为了保护患者隐私，我们使用图像修复技术设计了一个软件框架，该框架不需要使用唇裂图像进行训练，从而避免了模型泄漏的风险。我们实现了一种 novel 多任务架构，可以同时预测无唇裂面部图像和面部地标，结果由医生评估后表现出更好的性能。软件使用PyTorch实现，可以与消费级彩色图像以快速预测速度使用，从而实现有效的部署。
URL
https://arxiv.org/abs/2305.10589
