Jun 13, 2019 · 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.

Oct 8, 2019 · 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 ...

In this work, we propose to use lesion masks to generate synthetic lesion images in order to augment the segmentation training dataset and improve skin lesion ...

This repository provides the code and the model weights for our MICCAI SASHIMI 2019 paper: Mask2Lesion , a GAN-based paired image translation approach for ...

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 ...

Skin lesion segmentation is a vital task in skin cancer diagnosis and further treatment. Although deep learning based approaches have significantly improved ...

This dissertation uses generative adversarial networks to generate synthetic data to augment the classification model's training datasets to boost ...

In this paper, we propose a novel strategy to generate artificial lesions on non-lesion CT images so as to produce additional labeled training examples.

The semantic segmentation of skin lesions is an important and common initial task in the computer aided diagnosis of dermoscopic images. Lesion Segmentation ...

May 21, 2020 · Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis, MICCAI SASHIMI 2019; Illumination-based Transformations Improve Skin ...