Fábio Perez
Cited by
Cited by
Data augmentation for skin lesion analysis
F Perez, C Vasconcelos, S Avila, E Valle
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy …, 2018
Skin lesion synthesis with generative adversarial networks
A Bissoto, F Perez, E Valle, S Avila
OR 2.0 context-aware operating theaters, computer assisted robotic endoscopy …, 2018
Solo or ensemble? Choosing a CNN architecture for melanoma classification
F Perez, S Avila, E Valle
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Deep-learning ensembles for skin-lesion segmentation, analysis, classification: RECOD titans at ISIC challenge 2018
A Bissoto, F Perez, V Ribeiro, M Fornaciali, S Avila, E Valle
arXiv preprint arXiv:1808.08480, 2018
Weakly supervised active learning with cluster annotation
F Perez, R Lebret, K Aberer
arXiv preprint, 2018
Comparison of texture retrieval techniques using deep convolutional features
AC Valente, FVM Perez, GAS Megeto, MH Cascone, O Gomes, TS Paula, ...
Electronic Imaging 2019 (8), 406-1-406-7, 2019
Print defect mapping with semantic segmentation
A Valente, C Wada, D Neves, D Neves, F Perez, G Megeto, M Cascone, ...
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020
Labeling pixels having defects
Q Lin, AC Valente, OB Gomes, DG Neves, GAS Megeto, MH Cascone, ...
WO Patent WO2021061135A1, 2021
Print quality assessments via patch classification
Q Lin, OB Gomes, AC Valente, GAS Megeto, MH Cascone, ...
WO Patent WO2020131091A1, 2020
Deep learning for skin lesion classification: augment, train, and ensemble= Aprendizado profundo para classificação de lesões de pele: aumento, treino e conjunto
FVM Perez
[sn], 2019
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