Data Augmentation Using Adversarial Image-to-Image Translation for the Segmentation of Mobile-Acquired Dermatological Images

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021

Autores

Catarina Andrade (Autor) Outra; Luís F. Teixeira (Autor) FEUP; Maria João M. Vasconcelos (Autor) Outra; Luís Rosado (Autor) Outra

Revista

Título: Journal of Imaging

Vol. 20

Páginas: 1-2

Editora: MDPI

Indexação

ISI Web of Knowledge - 9 Citações

Outras Informações

ID Authenticus: P-00T-6YW

DOI: 10.3390/jimaging7010002

Abstract (EN): Dermoscopic images allow the detailed examination of subsurface characteristics of the skin, which led to creating several substantial databases of diverse skin lesions. However, the dermoscope is not an easily accessible tool in some regions. A less expensive alternative could be acquiring medium resolution clinical macroscopic images of skin lesions. However, the limited volume of macroscopic images available, especially mobile-acquired, hinders developing a clinical mobile-based deep learning approach. In this work, we present a technique to efficiently utilize the sizable number of dermoscopic images to improve the segmentation capacity of macroscopic skin lesion images. A Cycle-Consistent Adversarial Network is used to translate the image between the two distinct domains created by the different image acquisition devices. A visual inspection was performed on several databases for qualitative evaluation of the results, based on the disappearance and appearance of intrinsic dermoscopic and macroscopic features. Moreover, the Frechet Inception Distance was used as a quantitative metric. The quantitative segmentation results are demonstrated on the available macroscopic segmentation databases, SMARTSKINS and Dermofit Image Library, yielding test set thresholded Jaccard Index of 85.13% and 74.30%. These results establish a new state-of-the-art performance in the SMARTSKINS database.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15