# A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip

arXiv: https://arxiv.org/abs/2208.01149 · HTML: https://mlsanity.com/paper/2208.01149/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Image Inpainting via Conditional Texture and Structure Dual Generation](https://mlsanity.com/paper/2108.09760/) (2021)
- [Image Inpainting Guided by Coherence Priors of Semantics and Textures](https://mlsanity.com/paper/2012.08054/) (2020)
- [Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenes](https://mlsanity.com/paper/2103.15087/) (2021)
- [LaFIn: Generative Landmark Guided Face Inpainting](https://mlsanity.com/paper/1911.11394/) (2019)

## Built on by (most cited first, 2 of 2)

- [HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention](https://mlsanity.com/paper/2402.14185/) (2024)
- [INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network](https://mlsanity.com/paper/2305.10589/) (2023)

## Abstract (arXiv)

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 learning-based image inpainting method that is capable of covering a cleft lip and generating a lip and nose without a cleft. 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.