This paper presents a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the skin lesion segmentation task, which is a fundamental first step in the automated melanoma detection process. The proposed framework generates both skin lesion images and their segmentation masks, making the data augmentation process extremely straightforward. In order to thoroughly analyze how the quality and diversity of synthetic images impact the efficiency of the method, we remodel two different well known GANs: a Deep Convolutional GAN (DCGAN) and a Laplacian GAN (LAPGAN). Experimental results reveal that, by introducing such kind of synthetic data into the training process, the overall accuracy of a state-of-the-art Convolutional/Deconvolutional Neural Network for melanoma skin lesion segmentation is increased.

Augmenting data with GANs to segment melanoma skin lesions / Pollastri, Federico; Bolelli, Federico; Paredes Palacios, Roberto; Grana, Costantino. - In: MULTIMEDIA TOOLS AND APPLICATIONS. - ISSN 1380-7501. - 79:21-22(2020), pp. 15575-15592. [10.1007/s11042-019-7717-y]

Augmenting data with GANs to segment melanoma skin lesions

POLLASTRI, FEDERICO;Federico Bolelli
;
Paredes Palacios, Roberto;Costantino Grana
2020

Abstract

This paper presents a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the skin lesion segmentation task, which is a fundamental first step in the automated melanoma detection process. The proposed framework generates both skin lesion images and their segmentation masks, making the data augmentation process extremely straightforward. In order to thoroughly analyze how the quality and diversity of synthetic images impact the efficiency of the method, we remodel two different well known GANs: a Deep Convolutional GAN (DCGAN) and a Laplacian GAN (LAPGAN). Experimental results reveal that, by introducing such kind of synthetic data into the training process, the overall accuracy of a state-of-the-art Convolutional/Deconvolutional Neural Network for melanoma skin lesion segmentation is increased.
2020
18-mag-2019
79
21-22
15575
15592
Augmenting data with GANs to segment melanoma skin lesions / Pollastri, Federico; Bolelli, Federico; Paredes Palacios, Roberto; Grana, Costantino. - In: MULTIMEDIA TOOLS AND APPLICATIONS. - ISSN 1380-7501. - 79:21-22(2020), pp. 15575-15592. [10.1007/s11042-019-7717-y]
Pollastri, Federico; Bolelli, Federico; Paredes Palacios, Roberto; Grana, Costantino
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1176919
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