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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 2400+ citations to his publications and the advertisement in the mainstream media several times.

Interest
Topics
Details

Details

008
Publications

2018

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Authors
Zolfagharnasab, H; Bessa, S; Oliveira, SP; Faria, P; Teixeira, JF; Cardoso, JS; Oliveira, HP;

Publication
Sensors

Abstract

2018

The development of an automatic tool to improve perforators detection in Angio CT in DIEAP flap breast reconstruction

Authors
Mavioso, C; Correia Anacleto, JC; Vasconcelos, MA; Araujo, R; Oliveira, H; Pinto, D; Gouveia, P; Alves, C; Cardoso, F; Cardoso, J; Cardoso, MJ;

Publication
EUROPEAN JOURNAL OF CANCER

Abstract

2018

A deep learning approach for the forensic evaluation of sexual assault

Authors
Fernandes, K; Cardoso, JS; Astrup, BS;

Publication
Pattern Analysis and Applications

Abstract
Despite the existence of patterns able to discriminate between consensual and non-consensual intercourse, the relevance of genital lesions in the corroboration of a legal rape complaint is currently under debate in many countries. The testimony of the physicians when assessing these lesions has been questioned in court due to several factors (e.g., a lack of comprehensive knowledge of lesions, wide spectrum of background area, among others). Therefore, it is relevant to provide automated tools to support the decision process in an objective manner. In this work, we evaluate the performance of state-of-the-art deep learning architectures for the forensic assessment of sexual assault. We propose a deep architecture and learning strategy to tackle the class imbalance on deep learning using ranking. The proposed methodologies achieved the best results when compared with handcrafted feature engineering and with other deep architectures. © 2018 Springer-Verlag London Ltd., part of Springer Nature

2018

Automated Methods for the Decision Support of Cervical Cancer Screening Using Digital Colposcopies

Authors
Fernandes, K; Cardoso, JS; Fernandes, J;

Publication
IEEE ACCESS

Abstract
Cervical cancer remains a significant cause of mortality in low-income countries. However, it can often be cured by removing the affected tissues when detected in early stages. Therefore, it is relevant to provide universal and efficient access to cervical screening programs, being digital colposcopy an inexpensive technique with high potential of scalability. The development of computer-aided diagnosis systems for the automated processing of digital colposcopies has gained the attention of the computer vision and machine learning communities in the last decade, giving origin to a wide diversity of tasks and computational solutions. However, there is a lack of a unified framework to discuss the main tasks and to assess their performance. Thus, in this paper, we studied the core research lines surrounding the automated analysis of digital colposcopies and built a topology of problems and techniques, including their key properties, advantages, and limitations. Also, we discussed the open challenges in the area and released a database that serves as a common basis to evaluate such systems.

2018

Evolution, Current Challenges, and Future Possibilities in ECG Biometrics

Authors
Pinto, JR; Cardoso, JS; Lourenco, A;

Publication
IEEE ACCESS

Abstract
Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.

Supervised
thesis

2017

Performance assessment and prediction of football players: Tailoring an architecture with spatiotemporal positional and physiological features

Author
Sofia de Sousa Almeida

Institution
UP-FEUP

2017

Toward a 3D Planning Approach for Breast Conserving Surgery

Author
Hooshiar Zolfagharnasab

Institution
UP-FEUP

2017

Análise Automática de Melanoma Utilizando Imagens Dermatoscópicas

Author
Bruno Miguel Ferreira Moreira

Institution
UP-FEUP

2017

Portuguese Sign Language Recognition

Author
Pedro Miguel Martins Ferreira

Institution
UP-FEUP

2017

Análise e Classificação de Imagem Hiper-espectral

Author
Borgine Vasques Gurué

Institution
UP-FEUP