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    About

    About

    I am passionate about health, technology and entrepreneurship since child age. In this way, I started the Bioengineering degree at Faculty of Engineering of the University of Porto in 2012, ending the same in 2017. During my degree, I had inroads by research groups of INESC-TEC and I3S. I also founded a student branch chapter of the EMBS in UP in the year 2015, being president of the same for two years. In the last times, I worked at U. Porto Inovação as a technology analyst before joining INESC-TEC as a research fellow.

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    Publications
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    2019

    Convolutional Neural Network Architectures for Texture Classification of Pulmonary Nodules

    Authors
    Ferreira, CA; Cunha, A; Mendonça, AM; Campilho, A;

    Publication
    Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

    Abstract

    2019

    Wide Residual Network for Lung-Rads™ Screening Referral

    Authors
    Ferreira, CA; Aresta, G; Cunha, A; Mendonca, AM; Campilho, A;

    Publication
    2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

    Abstract

    2019

    Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

    Authors
    Ferreira, CA; Penas, S; Silva, J; Mendonca, AM;

    Publication
    2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

    Abstract

    2018

    Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2

    Authors
    Ferreira, CA; Melo, T; Sousa, P; Meyer, MI; Shakibapour, E; Costa, P; Campilho, A;

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

    Abstract
    Breast cancer is one of the leading causes of female death worldwide. The histological analysis of breast tissue allows for the differentiation of the tissue suspected to be abnormal into four classes: normal tissue, benign tumor, in situ carcinoma and invasive carcinoma. Automatic diagnostic systems can help in that task. In this sense, this work propose a deep neural network approach using transfer learning to classify breast cancer histology images. First, the added top layers are trained and a second fine-tunning is done on some feature extraction layers that are frozen previously. The used network is an Inception Resnet V2. In order to overcome the lack of data, data augmentation is performed too. This work is a suggested solution for the ICIAR 2018 BACH-Challenge and the accuracy is 0.76 in the blind test set. © 2018, Springer International Publishing AG, part of Springer Nature.

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