159 citations · 297 across the 4 of their papers we have counts for
4 papers
Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis
Eduardo Valle, Michel Fornaciali, Afonso Menegola +4
Deep learning fostered a leap ahead in automated skin lesion analysis in the last two years. Those models are expensive to train and difficult to parameterize. Objective: We invest…
Knowledge Transfer for Melanoma Screening with Deep Learning
Afonso Menegola, Michel Fornaciali, Ramon Pires +3
Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed fo…
RECOD Titans at ISIC Challenge 2017
Afonso Menegola, Julia Tavares, Michel Fornaciali +3
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). Althou…
Towards Automated Melanoma Screening: Exploring Transfer Learning Schemes
Afonso Menegola, Michel Fornaciali, Ramon Pires +2
Deep learning is the current bet for image classification. Its greed for huge amounts of annotated data limits its usage in medical imaging context. In this scenario transfer learn…