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About

Jaime S. Cardoso holds a Licenciatura (5-year degree) in Electrical and Computer Engineering in 1999, an MSc in Mathematical Engineering in 2005 and a Ph.D. in Computer Vision in 2006, all from the University of Porto.

Cardoso is an Associate Professor with Habilitation at the Faculty of Engineering of the University of Porto (FEUP), where he has been teaching Machine Learning and Computer Vision in Doctoral Programs and multiple courses for the graduate studies. Cardoso is currently a Senior Researcher of the ‘Information Processing and Pattern Recognition’ Area in the Telecommunications and Multimedia Unit of INESC TEC. He is also Senior Member of IEEE and co-founder of ClusterMedia Labs, an IT company developing automatic solutions for semantic audio-visual analysis.

His research can be summed up in three major topics: computer vision, machine learning and decision support systems.  Cardoso has co-authored 150+ papers, 50+ of which in international journals. Cardoso has been the recipient of numerous awards, including the Honorable Mention in the Exame Informática Award 2011, in software category, for project “Semantic PACS” and the First Place in the ICDAR 2013 Music Scores Competition: Staff Removal (task: staff removal with local noise), August 2013. The research results have been recognized both by the peers, with 6500+ citations to his publications and the advertisement in the mainstream media several times.

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016
Publications

2022

Lesion Volume Quantification Using Two Convolutional Neural Networks in MRIs of Multiple Sclerosis Patients

Authors
de Oliveira, M; Piacenti Silva, M; da Rocha, FCG; Santos, JM; Cardoso, JD; Lisboa, PN;

Publication
Diagnostics

Abstract
Background: Multiple sclerosis (MS) is a neurologic disease of the central nervous system which affects almost three million people worldwide. MS is characterized by a demyelination process that leads to brain lesions, allowing these affected areas to be visualized with magnetic resonance imaging (MRI). Deep learning techniques, especially computational algorithms based on convolutional neural networks (CNNs), have become a frequently used algorithm that performs feature self-learning and enables segmentation of structures in the image useful for quantitative analysis of MRIs, including quantitative analysis of MS. To obtain quantitative information about lesion volume, it is important to perform proper image preprocessing and accurate segmentation. Therefore, we propose a method for volumetric quantification of lesions on MRIs of MS patients using automatic segmentation of the brain and lesions by two CNNs. Methods: We used CNNs at two different moments: the first to perform brain extraction, and the second for lesion segmentation. This study includes four independent MRI datasets: one for training the brain segmentation models, two for training the lesion segmentation model, and one for testing. Results: The proposed brain detection architecture using binary cross-entropy as the loss function achieved a 0.9786 Dice coefficient, 0.9969 accuracy, 0.9851 precision, 0.9851 sensitivity, and 0.9985 specificity. In the second proposed framework for brain lesion segmentation, we obtained a 0.8893 Dice coefficient, 0.9996 accuracy, 0.9376 precision, 0.8609 sensitivity, and 0.9999 specificity. After quantifying the lesion volume of all patients from the test group using our proposed method, we obtained a mean value of 17,582 mm3 . Conclusions: We concluded that the proposed algorithm achieved accurate lesion detection and segmentation with reproducibility corresponding to state-of-the-art software tools and manual segmentation. We believe that this quantification method can add value to treatment monitoring and routine clinical evaluation of MS patients. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

2022

Tackling unsupervised multi-source domain adaptation with optimism and consistency

Authors
Pernes, D; Cardoso, JS;

Publication
Expert Systems with Applications

Abstract
It has been known for a while that the problem of multi-source domain adaptation can be regarded as a single source domain adaptation task where the source domain corresponds to a mixture of the original source domains. Nonetheless, how to adjust the mixture distribution weights remains an open question. Moreover, most existing work on this topic focuses only on minimizing the error on the source domains and achieving domain-invariant representations, which is insufficient to ensure low error on the target domain. In this work, we present a novel framework that addresses both problems and beats the current state of the art by using a mildly optimistic objective function and consistency regularization on the target samples. © 2022 Elsevier Ltd

2022

Streamlining Action Recognition in Autonomous Shared Vehicles with an Audiovisual Cascade Strategy

Authors
Pinto, JR; Carvalho, P; Pinto, C; Sousa, A; Capozzi, L; Cardoso, JS;

Publication
Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

Abstract

2022

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

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

Publication
CoRR

Abstract

2022

Quasi-Unimodal Distributions for Ordinal Classification

Authors
Albuquerque, T; Cruz, R; Cardoso, JS;

Publication
Mathematics

Abstract
Ordinal classification tasks are present in a large number of different domains. However, common losses for deep neural networks, such as cross-entropy, do not properly weight the relative ordering between classes. For that reason, many losses have been proposed in the literature, which model the output probabilities as following a unimodal distribution. This manuscript reviews many of these losses on three different datasets and suggests a potential improvement that focuses the unimodal constraint on the neighborhood around the true class, allowing for a more flexible distribution, aptly called quasi-unimodal loss. For this purpose, two constraints are proposed: A first constraint concerns the relative order of the top-three probabilities, and a second constraint ensures that the remaining output probabilities are not higher than the top three. Therefore, gradient descent focuses on improving the decision boundary around the true class in detriment to the more distant classes. The proposed loss is found to be competitive in several cases.

Supervised
thesis

2021

Deep Learning based Computer Aided Diagnosis for Breast Cancer Screening

Author
Eduardo Méca Castro

Institution
UP-FEUP

2021

Interpretable Machine Learning and its Application to Medical Decision Support Systems

Author
Tiago Filipe Sousa Gonçalves

Institution
UP-FEUP

2021

Mobile Target Detection and Tracking using Multiple Cooperative Aerial Robots

Author
Fábio André Costa Azevedo

Institution
UP-FEUP

2021

Label-efficient learning of LiDAR-based perception models for autonomous driving

Author
Bernardo Magina Madureira Palha de Araújo

Institution
UP-FEUP

2021

Self-explanatory computer-aided diagnosis with limited supervision

Author
Isabel Cristina Rio-Torto de Oliveira

Institution
UP-FEUP