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About

About

I'm Guilherme Aresta and I'm a PhD student/researcher at INESC TEC and Faculdade de Engenharia da Universidade do Porto (FEUP).

I've obtained my master degree in Bioengineering, field of Biomedical Engineering, at FEUP. 

My fields of interest are medical image analysis, computer vision and machine learning. My current research topic is the detection of lung nodules in computed tomography images.

Interest
Topics
Details

Details

001
Publications

2018

Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography

Authors
Aresta, G; Araújo, T; Jacobs, C; Ginneken, Bv; Cunha, A; Ramos, I; Campilho, A;

Publication
Image Analysis for Moving Organ, Breast, and Thoracic Images - Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings

Abstract

2018

UOLO - Automatic Object Detection and Segmentation in Biomedical Images

Authors
Araújo, T; Aresta, G; Galdran, A; Costa, P; Mendonça, AM; Campilho, A;

Publication
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - Lecture Notes in Computer Science

Abstract

2017

Classification of breast cancer histology images using Convolutional Neural Networks

Authors
Araujo, T; Aresta, G; Castro, E; Rouco, J; Aguiar, P; Eloy, C; Polonia, A; Campilho, A;

Publication
PLOS ONE

Abstract

2017

Improving convolutional neural network design via variable neighborhood search

Authors
Araujo, T; Aresta, G; Almada Lobo, B; Mendonca, AM; Campilho, A;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
An unsupervised method for convolutional neural network (CNN) architecture design is proposed. The method relies on a variable neighborhood search-based approach for finding CNN architectures and hyperparameter values that improve classification performance. For this purpose, t-Distributed Stochastic Neighbor Embedding (t-SNE) is applied to effectively represent the solution space in 2D. Then, k-Means clustering divides this representation space having in account the relative distance between neighbors. The algorithm is tested in the CIFAR-10 image dataset. The obtained solution improves the CNN validation loss by over 15% and the respective accuracy by 5%. Moreover, the network shows higher predictive power and robustness, validating our method for the optimization of CNN design. © Springer International Publishing AG 2017.

2017

Detection of juxta-pleural lung nodules in computed tomography images

Authors
Aresta, G; Cunha, A; Campilho, A;

Publication
Medical Imaging 2017: Computer-Aided Diagnosis

Abstract