# Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis

## TL;DR

This paper introduces Mask2Lesion, a GAN-based method that generates synthetic skin lesion images from masks to augment training data, significantly improving segmentation accuracy in skin cancer diagnosis.

## Contribution

The novel Mask2Lesion model leverages segmentation masks to produce realistic lesion images, enhancing dataset diversity and segmentation performance.

## Key findings

- Achieved a 5.17% increase in mean Dice score with Mask2Lesion augmentation.
- Demonstrated effectiveness on the ISIC 2017 dataset.
- Outperformed classical data augmentation techniques.

## Abstract

Skin lesion segmentation is a vital task in skin cancer diagnosis and further treatment. Although deep learning based approaches have significantly improved the segmentation accuracy, these algorithms are still reliant on having a large enough dataset in order to achieve adequate results. Inspired by the immense success of generative adversarial networks (GANs), we propose a GAN-based augmentation of the original dataset in order to improve the segmentation performance. In particular, we use the segmentation masks available in the training dataset to train the Mask2Lesion model, and use the model to generate new lesion images given any arbitrary mask, which are then used to augment the original training dataset. We test Mask2Lesion augmentation on the ISBI ISIC 2017 Skin Lesion Segmentation Challenge dataset and achieve an improvement of 5.17% in the mean Dice score as compared to a model trained with only classical data augmentation techniques.