Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About
Download Photo HD

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. Since 2018 he has been treasurer of the Portuguese section of IEEE EMBS. In 2019, he received funding from the FCT for PhD and became Business Development Manager on TEC4Health at INESC TEC.

Interest
Topics
Details

Details

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

2020

Classification of Lung Nodules in CT Volumes Using the Lung-RADS™ Guidelines with Uncertainty Parameterization

Authors
Ferreira, CA; Aresta, G; Pedrosa, J; Rebelo, J; Negrão, E; Cunha, A; Ramos, I; Campilho, A;

Publication
17th IEEE International Symposium on Biomedical Imaging, ISBI 2020, Iowa City, IA, USA, April 3-7, 2020

Abstract
Currently, lung cancer is the most lethal in the world. In order to make screening and follow-up a little more systematic, guidelines have been proposed. Therefore, this study aimed to create a diagnostic support approach by providing a patient label based on the LUNG-RADS™ guidelines. The only input required by the system is the nodule centroid to take the region of interest for the input of the classification system. With this in mind, two deep learning networks were evaluated: a Wide Residual Network and a DenseNet. Taking into account the annotation uncertainty we proposed to use sample weights that are introduced in the loss function, allowing nodules with a high agreement in the annotation process to take a greater impact on the training error than its counterpart. The best result was achieved with the Wide Residual Network with sample weights achieving a nodule-wise LUNG-RADS™ labelling accuracy of 0.735\pm 0.003. © 2020 IEEE.

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