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    About

    Pedro Neto is a MSc in Computer Science from the Aalto University, Finland and a PhD candidate at FEUP. Simultaneously, he works as a research assistant at Centre of Telecommunication and Multimedia at INESC TEC, developing, as part of the CADPath project, computer-aided diagnosis systems for colorectal and cervical cancers. Besides his work on the project, Pedro is also researching biometric systems, for instance face recognition or presentation attack detection, as well as the interpretability and explainability of artificial intelligence models.

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    Publications
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    2022

    Myope Models - Are face presentation attack detection models short-sighted?

    Authors
    Neto, PC; Sequeira, AF; Cardoso, JS;

    Publication
    2022 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION WORKSHOPS (WACVW 2022)

    Abstract

    2022

    iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images

    Authors
    Neto, PC; Oliveira, SP; Montezuma, D; Fraga, J; Monteiro, A; Ribeiro, L; Goncalves, S; Pinto, IM; Cardoso, JS;

    Publication
    CANCERS

    Abstract
    Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.

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    2022

    Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition

    Authors
    Neto, PCP; Pinto, JR; Boutros, F; Damer, N; Sequeira, AF; Cardoso, JS;

    Publication
    IEEE ACCESS

    Abstract

    2022

    Quality Control in Digital Pathology: Automatic Fragment Detection and Counting

    Authors
    Albuquerque, T; Moreira, A; Barros, B; Montezuma, D; Oliveira, SP; Neto, PC; Monteiro, J; Ribeiro, L; Goncalves, S; Monteiro, A; Pinto, IM; Cardoso, JS;

    Publication
    2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

    Abstract

    2022

    OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement

    Authors
    Neto, PC; Goncalves, T; Huber, M; Damer, N; Sequeira, AF; Cardoso, JS;

    Publication
    2022 International Conference of the Biometrics Special Interest Group (BIOSIG)

    Abstract