INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network
This addresses a privacy-sensitive medical imaging need for cleft lip patients and surgeons, but is incremental as it applies existing inpainting techniques to a specific domain.
The paper tackles the problem of generating non-cleft facial images for cleft lip patients to aid surgical discussions, using an image inpainting approach that avoids training on sensitive data and achieves better performance as evaluated by surgeons.
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.