论文标题
部分可观测时空混沌系统的无模型预测
A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
论文作者
论文摘要
唇裂是一种先天性异常，需要专家手术修复。外科医生必须具有丰富的经验和理论知识来进行手术，并且已经提出了人工智能（AI）方法来指导外科医生改善手术结局。如果可以使用AI来预测修复的唇唇的外观，那么外科医生可以将其用作辅助手术技术并改善结果。为了在保护患者隐私时探索这个想法的可行性，我们提出了一种基于深度学习的图像indpainting方法，该方法能够覆盖唇裂，并在没有裂缝的情况下产生嘴唇和鼻子。我们的实验是在两个现实世界中的裂口数据集上进行的，并由专家cleft唇外科医生评估，以证明该方法的可行性。
A Cleft lip is a congenital abnormality requiring surgical repair by a specialist. The surgeon must have extensive experience and theoretical knowledge to perform surgery, and Artificial Intelligence (AI) method has been proposed to guide surgeons in improving surgical outcomes. If AI can be used to predict what a repaired cleft lip would look like, surgeons could use it as an adjunct to adjust their surgical technique and improve results. To explore the feasibility of this idea while protecting patient privacy, we propose a deep learning-based image inpainting method that is capable of covering a cleft lip and generating a lip and nose without a cleft. Our experiments are conducted on two real-world cleft lip datasets and are assessed by expert cleft lip surgeons to demonstrate the feasibility of the proposed method.
