@article{CHEN2023100517,
title = {INCLG: Inpainting for non-cleft lip generation with a multi-task image processing network},
journal = {Software Impacts},
volume = {17},
pages = {100517},
year = {2023},
issn = {2665-9638},
doi = {https://doi.org/10.1016/j.simpa.2023.100517},
url = {https://www.sciencedirect.com/science/article/pii/S2665963823000544},
author = {Shuang Chen and Amir Atapour-Abarghouei and Edmond S.L. Ho and Hubert P.H. Shum},
keywords = {Cleft lip, Image inpainting, Deep neural network, Multi-task learning, Face modeling},
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.}
}