Name
Abstract | ||
|---|---|---|
This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 Skin Lesion Analysis Towards Melanoma Detection (ISBI 2017). Although our team has a long experience with melanoma classification, the ISIC Challenge 2017 was the very first time we worked on skin-lesion segmentation. For part 1 (segmentation), our final submission used four of our models: two trained with all 2000 samples, without a validation split, for 250 and for 500 epochs respectively; and other two trained and validated with two different 1600/400 splits, for 220 epochs. Those four models, individually, achieved between 0.780 and 0.783 official validation scores. Our final submission averaged the output of those four models achieved a score of 0.793. For part 3 (classification), the submitted test run as well as our last official validation run were the result from a meta-model that assembled seven base deep-learning models: three based on Inception-V4 trained on our largest dataset; three based on Inception trained on our smallest dataset; and one based on ResNet-101 trained on our smaller dataset. The results of those component models were stacked in a meta-learning layer based on an SVM trained on the validation set of our largest dataset. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Skin lesion,Pattern recognition,Computer science,Segmentation,Support vector machine,Artificial intelligence,Melanoma detection,Machine learning |
DocType | Volume | Citations |
Journal | abs/1703.04819 | 10 |
PageRank | References | Authors |
0.50 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Afonso Menegola | 1 | 30 | 2.54 |
| Julia Tavares | 2 | 14 | 1.36 |
| Michel Fornaciali | 3 | 39 | 3.94 |
| Lin Tzy Li | 4 | 67 | 9.30 |
| Sandra Eliza Fontes de Avila | 5 | 377 | 15.15 |
| Eduardo Valle | 6 | 373 | 22.17 |