Colpo-Train: A Computer-Aided Learning Based on Generative Adversarial Network for Colposcopy Training

Colposcopy is a medical examination done using an optical tool in order to inspect the lesions in the patient's cervix. It is mainly recommended to screen for cervical cancer. Due to privacy and cultural concerns, images of colposcopy are hard to find, which makes colposcopy training examples very limited. This paper aims to present a Generative Adversarial Network (GAN) based system that anonymizes and generates synthetic images of colposcopy for medical student training. The proposed system can be used by medical teachers and trainers in order to present clinical cases using fake images generated using a GAN. The latter is trained using a dataset of real images of colposcopy labelled by a domain expert. Our current prototype is a web- based application wherein the students can visualise colposcopy images and then have to choose the right diagnostics, which have been previously defined by the teachers. The prototype has shown interesting feedback from the students and the teachers; however, the generated image's quality needs to be enhanced.