INCLG: Inpainting for non-cleft lip generation with a multi-task image processing network

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.

Automated summary

We introduce a software that predicts non-cleft facial images for patients with cleft lip, facilitating the understanding and discussion of cleft lip surgeries. To protect privacy, we design a software framework using image inpainting, which doesn't require cleft lip images for training, mitigating the risk of leakage. We implement a novel multi-task architecture that predicts both non-cleft facial images and facial landmarks, resulting in improved performance as evaluated by surgeons. The software is implemented with PyTorch, supporting consumer-level color images and offering fast prediction speed for effective deployment.