Name
Abstract | ||
|---|---|---|
Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly. Currently, Deep Neural Networks are the state-of-the-art results on automated skin cancer classification. To push the results further, we need to address the lack of annotated data, which is expensive and require much effort from specialists. To bypass this problem, we propose using Generative Adversarial Networks for generating realistic synthetic skin lesion images. To the best of our knowledge, our results are the first to show visually-appealing synthetic images that comprise clinically-meaningful information. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-01201-4_32 | OR 2.0 CONTEXT-AWARE OPERATING THEATERS, COMPUTER ASSISTED ROBOTIC ENDOSCOPY, CLINICAL IMAGE-BASED PROCEDURES, AND SKIN IMAGE ANALYSIS, OR 2.0 2018 |
Keywords | DocType | Volume |
Skin cancer, Generative models, Deep learning | Journal | 11041 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.39 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alceu Bissoto | 1 | 3 | 1.75 |
| Fábio Perez | 2 | 12 | 1.96 |
| Eduardo Valle | 3 | 25 | 2.48 |
| Sandra De Avila | 4 | 38 | 8.96 |