[MOCK MOLE: PRODUCING SYNTHETIC IMAGES THAT RECAPITULATE CONFOCAL PATTERNS OF MELANOCYTIC NEVI VIA DEEP-LEARNING
Miriam Ben Shachar1, Aviv Yampolsky2, Mor Benayoun2
1Kittner Skin Cancer Screening and Research Institute, Medical Screening Institute, Sheba Medical Center, Adelson School of Medicine, Ariel University, Arrow Program for Medical Research Education, Sheba Medical Center, Ramat-Gan, Israel.
Introduction:
Melanocytic nevi present microscopic patterns, which differ in their associated melanoma risk, and can be non-invasively recognized under Reflectance Confocal Microscopy (RCM).
Aims:
To train a Generative Adversarial Network (GAN) deep-learning model to produce synthetic images that recapitulate RCM patterns of nevi, enabling reliable classification by human readers and by a Convolutional Neural Network (CNN) computer model.
Methods:
A dataset of RCM images of nevi, presenting a uniform pattern, were chosen and classified into one of three patterns - Meshwork, Ring or Clod. Images were used for training a GAN model, which in turn, produced synthetic images recapitulating RCM patterns of nevi. A random sample of synthetic images was classified by two independent human readers and by a CNN model. Human and computer-model classifications were compared.
Results:
The training set for the GAN model included 1496 RCM images, including 977 images (65.3%) with Meshwork pattern, 261 (17.4%) with Ring and 258 (17.2%) with Clod pattern. The GAN model produced 6000 synthetic RCM-like images. Of these, 302 images were randomly chosen and classified by human readers, including 83 (27.5%) classified as Meshwork, 131 (43.4%) as Ring, and 88 (29.1%) as Clod pattern. Human inter-observer concordance in pattern classification was 91.7%, and human-to-CNN concordance was 87.7%.
Conclusions:
We demonstrate feasibility of producing synthetic images, which recapitulate RCM patterns of nevi and can be reproducibly recognized by human readers and by deep-learning models. Synthetic image datasets may allow teaching RCM patterns to novices, training of computer models, and data sharing between research centers.
