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    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.

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    Publications
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    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.

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    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