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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 PhD and became Business Development Manager on TEC4Health at INESC TEC. Finally, Carlos has been elected treasurer in the IEEE, first from 2018-2021 in the EMBS PT chapter and since 2022 in the Portugal section.

Interest
Topics
Details

Details

  • Name

    Carlos Alexandre Ferreira
  • Role

    Business Developer
  • Since

    06th September 2017
  • Nationality

    Portugal
  • Contacts

    +351222094000
    carlos.a.ferreira@inesctec.pt
004
Publications

2022

Computer-aided lung cancer screening in computed tomography: state-of the-art and future perspectives

Authors
Pedrosa, J; Aresta, G; Ferreira, C;

Publication
Detection Systems in Lung Cancer and Imaging, Volume 1

Abstract

2022

Assessing clinical applicability of COVID-19 detection in chest radiography with deep learning

Authors
Pedrosa, J; Aresta, G; Ferreira, C; Carvalho, C; Silva, J; Sousa, P; Ribeiro, L; Mendonca, AM; Campilho, A;

Publication
SCIENTIFIC REPORTS

Abstract
The coronavirus disease 2019 (COVID-19) pandemic has impacted healthcare systems across the world. Chest radiography (CXR) can be used as a complementary method for diagnosing/following COVID-19 patients. However, experience level and workload of technicians and radiologists may affect the decision process. Recent studies suggest that deep learning can be used to assess CXRs, providing an important second opinion for radiologists and technicians in the decision process, and super-human performance in detection of COVID-19 has been reported in multiple studies. In this study, the clinical applicability of deep learning systems for COVID-19 screening was assessed by testing the performance of deep learning systems for the detection of COVID-19. Specifically, four datasets were used: (1) a collection of multiple public datasets (284.793 CXRs); (2) BIMCV dataset (16.631 CXRs); (3) COVIDGR (852 CXRs) and 4) a private dataset (6.361 CXRs). All datasets were collected retrospectively and consist of only frontal CXR views. A ResNet-18 was trained on each of the datasets for the detection of COVID-19. It is shown that a high dataset bias was present, leading to high performance in intradataset train-test scenarios (area under the curve 0.55–0.84 on the collection of public datasets). Significantly lower performances were obtained in interdataset train-test scenarios however (area under the curve > 0.98). A subset of the data was then assessed by radiologists for comparison to the automatic systems. Finetuning with radiologist annotations significantly increased performance across datasets (area under the curve 0.61–0.88) and improved the attention on clinical findings in positive COVID-19 CXRs. Nevertheless, tests on CXRs from different hospital services indicate that the screening performance of CXR and automatic systems is limited (area under the curve < 0.6 on emergency service CXRs). However, COVID-19 manifestations can be accurately detected when present, motivating the use of these tools for evaluating disease progression on mild to severe COVID-19 patients. © 2022, The Author(s).

2021

LNDb Challenge on automatic lung cancer patient management

Authors
Pedrosa, J; Aresta, G; Ferreira, C; Atwal, G; Phoulady, HA; Chen, XY; Chen, RZ; Li, JL; Wang, LS; Galdran, A; Bouchachia, H; Kaluva, KC; Vaidhya, K; Chunduru, A; Tarai, S; Nadimpalli, SPP; Vaidya, S; Kim, I; Rassadin, A; Tian, ZH; Sun, ZW; Jia, YZ; Men, XJ; Ramos, I; Cunha, A; Campilho, A;

Publication
MEDICAL IMAGE ANALYSIS

Abstract

2021

A multi-task CNN approach for lung nodule malignancy classification and characterization

Authors
Marques, S; Schiavo, F; Ferreira, CA; Pedrosa, J; Cunha, A; Campilho, A;

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract

2021

Ovarian Structures Detection using Convolutional Neural Networks

Authors
Wanderley, DS; Ferreira, CA; Campilho, A; Silva, JA;

Publication
CENTERIS 2021 - International Conference on ENTERprise Information Systems / ProjMAN 2021 - International Conference on Project MANagement / HCist 2021 - International Conference on Health and Social Care Information Systems and Technologies 2021, Braga, Portugal

Abstract