mlsanityData to 7 Oct 2026

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

arXiv 2208.01149 ↗First posted 1 Aug 2022cs.CVcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 19 references that are in the map. First posted on arXiv 1 Aug 2022.

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9 of 19 references.

References · 9

  1. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  2. 2021Alias-Free Generative Adversarial Networkscited by 1,181
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2018Free-Form Image Inpainting with Gated Convolutioncited by 594
  5. 2018Image Inpainting for Irregular Holes Using Partial Convolutionscited by 505
  6. 2021Image Inpainting via Conditional Texture and Structure Dual Generationcited by 66
  7. 2020Image Inpainting Guided by Coherence Priors of Semantics and Texturescited by 37
  8. 2021Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenescited by 25
  9. 2019LaFIn: Generative Landmark Guided Face Inpaintingcited by 12

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Citing papers per year: 2023: 1; 2024: 1.

2 papers.