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About

Carlos Ferreira is passionate about health, technology and entrepreneurship since child age. In this way, he started the Bioengineering degree at Faculty of Engineering of the University of Porto in 2012, ending the same in 2017. During his degree, he had inroads by research groups of INESC-TEC and I3S. He also founded a student branch chapter of the EMBS in UP in the year 2015, being chair of the same for two years, and vice chair of NEB FEUP / ICBAS during 2016/2017. In 2017, he worked at U. Porto Inovação as a technology analyst before joining INESC TEC as a researcher in the field of medical image analysis for the classification of pulmonary nodules in computed tomography. In 2019, he received funding from the FCT for his PhD and became Business Development Manager on TEC4Health at INESC TEC.

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002
Publications

2020

Automatic Lung Reference Model

Authors
Machado, M; Ferreira, CA; Pedrosa, J; Negrão, E; Rebelo, J; Leitão, P; Carvalho, AS; Rodrigues, MC; Ramos, I; Cunha, A; Campilho, A;

Publication
IFMBE Proceedings - XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019

Abstract

2020

Automatic lung nodule detection combined with gaze information improves radiologists' screening performance

Authors
Aresta, G; Ramos, I; Campilho, A; Ferreira, C; Pedrosa, J; Araujo, T; Rebelo, J; Negrao, E; Morgado, M; Alves, F; Cunha, A;

Publication
IEEE Journal of Biomedical and Health Informatics

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.