Name
Title | ||
|---|---|---|
Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018. |
Abstract | ||
|---|---|---|
This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 Skin Lesion Analysis Towards Melanoma Detection (MICCAI 2018). Although our team has a long experience with melanoma classification and moderate experience with lesion segmentation, the ISIC Challenge 2018 was the very first time we worked on lesion attribute detection. For each task we submitted 3 different ensemble approaches, varying combinations of models and datasets. Our best results on the official testing set, regarding the official metric of each task, were: 0.728 (segmentation), 0.344 (attribute detection) and 0.803 (classification). Those submissions reached, respectively, the 56th, 14th and 9th places. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Lesion,Skin lesion,Pattern recognition,Segmentation,Computer science,Melanoma detection,Artificial intelligence,Deep learning,Lesion segmentation,Machine learning |
DocType | Volume | Citations |
Journal | abs/1808.08480 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alceu Bissoto | 1 | 3 | 1.75 |
| Fábio Perez | 2 | 12 | 1.96 |
| Vinícius Ribeiro | 3 | 0 | 0.34 |
| Michel Fornaciali | 4 | 39 | 3.94 |
| Sandra De Avila | 5 | 38 | 8.96 |
| Eduardo Valle | 6 | 373 | 22.17 |