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

Chen, S., Atapour-Abarghouei, A., Ho, E. S.L. and Shum, H. P.H. (2023) INCLG: inpainting for non-cleft lip generation with a multi-task image processing network. Software Impacts, 17, 100517. (doi: 10.1016/j.simpa.2023.100517)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ho, Dr Edmond S. L
• Authors: Chen, S., Atapour-Abarghouei, A., Ho, E. S.L., and Shum, H. P.H.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Software Impacts
• Publisher: Elsevier
• ISSN: 2665-9638
• ISSN (Online): 2665-9638
• Published Online: 22 May 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Software Impact 17: 100517
• Publisher Policy: Reproduced under a Creative Commons License