  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.
