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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Hair removal in dermoscopy images using variational autoencoders.

Dalal Bardou1, Hamida Bouaziz2, Laishui Lv3

  • 1Department of Computer Science and Mathematics, University of Abbes Laghrour, Khenchela, Algeria.

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)
|March 7, 2022
PubMed
Summary

This study presents an efficient variational autoencoder method for removing hair from dermoscopy images, improving melanoma detection accuracy. The technique effectively reconstructs hair-free images while preserving texture and visual quality.

Keywords:
dermoscopy imageshair occlusionhair removalperceptual lossvariational autoencoders

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dermatology

Background:

  • Melanoma incidence is increasing, highlighting the need for accurate early detection methods.
  • Dermoscopy is crucial for melanoma diagnosis, but hair occlusion in images poses a significant challenge.
  • Digital dermoscopy images are increasingly used for automated melanoma detection, necessitating hair removal techniques.

Purpose of the Study:

  • To develop an efficient and simple method for hair removal from dermoscopy images.
  • To improve the accuracy of automated melanoma detection by eliminating hair artifacts.
  • To enhance the visual quality of reconstructed hair-free dermoscopy images.

Main Methods:

  • A variational autoencoder (VAE) model was employed for hair removal without requiring paired samples.
  • The VAE encoder learns a latent representation that ignores hair, while the decoder reconstructs hair-free images.
  • A two-stage training process and three loss functions (SSIM, L1, L2) were utilized to optimize image quality and texture preservation.

Main Results:

  • The proposed VAE method successfully generated hair-free dermoscopy images.
  • Evaluation using t-distributed stochastic neighbor embedding (SNE) demonstrated the efficiency of the method.
  • Experiments on the HAM10000 dataset confirmed the effectiveness of the hair removal technique.

Conclusions:

  • The developed variational autoencoder offers a promising solution for hair removal in dermoscopy images.
  • This technique can significantly aid in improving the accuracy of computer-aided diagnosis for melanoma.
  • The method's ability to preserve image quality makes it suitable for clinical applications.