| Full text | |
| Author(s): |
Bissoto, Alceu
;
Valle, Eduardo
;
Avila, Sandra
;
IEEE Comp Soc
Total Authors: 4
|
| 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 |