Open AccessHair removal in dermoscopy images using variational autoencoders
Author(s) -
Bardou Dalal,
Bouaziz Hamida,
Lv Laishui,
Zhang Ting
Publication year - 2022
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/srt.13145
Subject(s) - autoencoder , artificial intelligence , computer science , pattern recognition (psychology) , computer vision , deep learning , noise (video) , convolutional neural network , image quality , skin cancer , image (mathematics) , medical imaging , cancer , medicine
Background In recent years, melanoma is rising at a faster rate compared to other cancers. Although it is the most serious type of skin cancer, the diagnosis at early stages makes it curable. Dermoscopy is a reliable medical technique used to detect melanoma by using a dermoscope to examine the skin. In the last few decades, digital imaging devices have made great progress which allowed capturing and storing high‐quality images from these examinations. The stored images are now being standardized and used for the automatic detection of melanoma. However, when the hair covers the skin, this makes the task challenging. Therefore, it is important to eliminate the hair to get accurate results. Methods In this paper, we propose a simple yet efficient method for hair removal using a variational autoencoder without the need for paired samples. The encoder takes as input a dermoscopy image and builds a latent distribution that ignores hair as it is considered noise, while the decoder reconstructs a hair‐free image. Both encoder and decoder use a decent convolutional neural networks architecture that provides high performance. The construction of our model comprises two stages of training. In the first stage, the model has trained on hair‐occluded images to output hair‐free images, and in the second stage, it is optimized using hair‐free images to preserve the image textures. Although the variational autoencoder produces hair‐free images, it does not maintain the quality of the generated images. Thus, we explored the use of three‐loss functions including the structural similarity index (SSIM), L1‐norm, and L2‐norm to improve the visual quality of the generated images. Results The evaluation of the hair‐free reconstructed images is carried out using t‐distributed stochastic neighbor embedding (SNE) feature mapping by visualizing the distribution of the real hair‐free images and the synthesized hair‐free images. The conducted experiments on the publicly available dataset HAM10000 show that our method is very efficient.