Chris Chen shuang.chen@durham.ac.uk
PGR Student Doctor of Philosophy
Chris Chen shuang.chen@durham.ac.uk
PGR Student Doctor of Philosophy
Dr Amir Atapour-Abarghouei amir.atapour-abarghouei@durham.ac.uk
Assistant Professor
Jane Kerby
Edmond S.L. Ho
David C.G. Sainsbury
Sophie Butterworth
Dr Hubert Shum hubert.shum@durham.ac.uk
Associate Professor
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 learningbased image inpainting method that is capable of covering a cleft lip and generating a lip and nose without a celft. 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.
Chen, S., Atapour-Abarghouei, A., Kerby, J., Ho, E. S., Sainsbury, D. C., Butterworth, S., & Shum, H. P. (2022). A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip. . https://doi.org/10.1109/bhi56158.2022.9926917
Conference Name | 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) |
---|---|
Conference Location | Ioannina, Greece |
Start Date | Sep 27, 2022 |
End Date | Sep 30, 2022 |
Acceptance Date | Jul 19, 2022 |
Online Publication Date | Nov 4, 2022 |
Publication Date | 2022 |
Deposit Date | Aug 1, 2022 |
Publicly Available Date | Oct 1, 2022 |
Series ISSN | 2641-3604,2641-3590 |
DOI | https://doi.org/10.1109/bhi56158.2022.9926917 |
Accepted Conference Proceeding
(746 Kb)
PDF
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
(2023)
Conference Proceeding
Region-based Appearance and Flow Characteristics for Anomaly Detection in Infrared Surveillance Imagery
(2023)
Conference Proceeding
Hierarchical Graph Convolutional Networks for Action Quality Assessment
(2023)
Journal Article
UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-Identification in Video Imagery
(2022)
Conference Proceeding
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search