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    João Pedrosa was born in Figueira da Foz, Portugal, in 1990. He received the M.Sc. degree in biomedical engineering from the University of Porto, Porto, Portugal, in 2013 and the Ph.D. degree in biomedical sciences with KU Leuven, Leuven, Belgium, in 2018. He is currently a postdoctoral researcher at INESC TEC, Porto Portugal working on image processing and computer-aided diagnosis in lung cancer CT screening and diabetic retinopathy. His research interests include medical imaging acquisition and processing, machine learning and applied research for improved patient care.

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    2020

    Automatic Lung Reference Model

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

    Publication
    IEEE Journal of Biomedical and Health Informatics

    Abstract

    2020

    LNDetector: A Flexible Gaze Characterisation Collaborative Platform for Pulmonary Nodule Screening

    Authors
    Pedrosa, J; Aresta, G; Rebelo, J; Negrao, E; Ramos, I; Cunha, A; Campilho, A;

    Publication
    IFMBE Proceedings

    Abstract
    Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening. © 2020, Springer Nature Switzerland AG.

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    2020

    A novel 2D speckle tracking method for high frame rate echocardiography

    Authors
    Orlowska, M; Ramalli, A; Petrescu, A; Cvijic, M; Bezy, S; Santos, P; Pedrosa, J; Voigt, J; D'hooge, J;

    Publication
    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control

    Abstract

    2019

    Velocities of Naturally Occurring Myocardial Shear Waves Increase With Age and in Cardiac Amyloidosis

    Authors
    Petrescu, A; Santos, P; Orlowska, M; Pedrosa, J; Bézy, S; Chakraborty, B; Cvijic, M; Dobrovie, M; Delforge, M; D'hooge, J; Voigt, JU;

    Publication
    JACC: Cardiovascular Imaging

    Abstract