activity
20172021
most citedRecent advances in deep learning applied to skin cancer detection

11 citations · 12 across the 4 of their papers we have counts for

collaborators

6 papers

cs.SD2021

Improving Deep Learning Sound Events Classifiers using Gram Matrix Feature-wise Correlations

Antonio Joia Neto, Andre G C Pacheco, Diogo C Luvizon

In this paper, we propose a new Sound Event Classification (SEC) method which is inspired in recent works for out-of-distribution detection. In our method, we analyse all the activ…

eess.IV20201 cited

PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones

Andre G. C. Pacheco, Gustavo R. Lima, Amanda S. Salomão +16

Over the past few years, different computer-aided diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images s…

eess.IV201911 cited

Recent advances in deep learning applied to skin cancer detection

Andre G. C. Pacheco, Renato A. Krohling

Skin cancer is a major public health problem around the world. Its early detection is very important to increase patient prognostics. However, the lack of qualified professionals a…

eess.IV2019

The impact of patient clinical information on automated skin cancer detection

Andre G. C. Pacheco, Renato A. Krohling

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonethele…

cs.LG2019

Skin cancer detection based on deep learning and entropy to detect outlier samples

Andre G. C. Pacheco, Abder-Rahman Ali, Thomas Trappenberg

We describe our methods that achieved the 3rd and 4th places in tasks 1 and 2, respectively, at ISIC challenge 2019. The goal of this challenge is to provide the diagnostic for ski…

cs.NE2017

Restricted Boltzmann machine to determine the input weights for extreme learning machines

Andre Pacheco, Renato Krohling, Carlos da Silva

The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assi…