GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review

Author(s)

Bissoto, Alceu ; Valle, Eduardo ; Avila, Sandra ; IEEE Comp Soc

• Document type: Journal article
• Source: 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGITION WORKSHOPS (CVPRW 2021); v. N/A, p. 10-pg., 2021-01-01.

Abstract

Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis. Generative Adversarial Networks (GANs) appear as an enticing alternative to alleviate the issue, by synthesizing samples indistinguishable from real images, with a plethora of works employing them for medical applications. Nevertheless, carefully designed experiments for skin-lesion diagnosis with GAN-based data augmentation show favorable results only on out-of-distribution test sets. For GAN-based data anonymization - where the synthetic images replace the real ones - favorable results also only appear for out-of-distribution test sets. Because of the costs and risks associated with GAN usage, those results suggest caution in their adoption for medical applications. (AU)

• FAPESP's process: 19/19619-7 - Generating unlimited skin lesion images with generative adversarial networks
• Grantee: Alceu Emanuel Bissoto
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/08293-7 - CCES - Center for Computational Engineering and Sciences
• Grantee: Munir Salomao Skaf
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC