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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.

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Publications

2020

Learning signer-invariant representations with adversarial training

Authors
Ferreira, PM; Pernes, D; Rebelo, A; Cardoso, JS;

Publication
Twelfth International Conference on Machine Vision (ICMV 2019)

Abstract

2020

Automatic detection of perforators for microsurgical reconstruction

Authors
Mavioso, C; Araujo, RJ; Oliveira, HP; Anacleto, JC; Vasconcelos, MA; Pinto, D; Gouveia, PF; Alves, C; Cardoso, F; Cardoso, JS; Cardoso, MJ;

Publication
The Breast

Abstract

2020

Deep Aesthetic Assessment of Breast Cancer Surgery Outcomes

Authors
Gonçalves, T; Silva, W; Cardoso, J;

Publication
IFMBE Proceedings

Abstract
Breast cancer is a highly mutable and rapidly evolving disease, with a large worldwide incidence. Even though, it is estimated that approximately 90% of the cases are treatable and curable if detected on early staging and given the best treatment. Nowadays, with the existence of breast cancer routine screening habits, better clinical treatment plans and proper management of the disease, it is possible to treat most cancers with conservative approaches, also known as breast cancer conservative treatments (BCCT). With such a treatment methodology, it is possible to focus on the aesthetic results of the surgery and the patient’s Quality of Life, which may influence BCCT outcomes. In the past, this assessment would be done through subjective methods, where a panel of experts would be needed to perform the assessment; however, with the development of computer vision techniques, objective methods, such as BAT© and BCCT.core, which perform the assessment based on asymmetry measurements, have been used. On the other hand, they still require information given by the user and none of them has been considered the gold standard for this task. Recently, with the advent of deep learning techniques, algorithms capable of improving the performance of traditional methods on the detection of breast fiducial points (required for asymmetry measurements) have been proposed and showed promising results. There is still, however, a large margin for investigation on how to integrate such algorithms in a complete application, capable of performing an end-to-end classification of the BCCT outcomes. Taking this into account, this thesis shows a comparative study between deep convolutional networks for image segmentation and two different quality-driven keypoint detection architectures for the detection of the breast contour. One that uses a deep learning model that has learned to predict the quality (given by the mean squared error) of an array of keypoints, and, based on this quality, applies the backpropagation algorithm, with gradient descent, to improve them; another which uses a deep learning model which was trained with the quality as a regularization method and that used iterative refinement, in each training step, to improve the quality of the keypoints that were fed into the network. Although none of the methods surpasses the current state of the art, they present promising results for the creation of alternative methodologies to address other regression problems in which the learning of the quality metric may be easier. Following the current trend in the field of web development and with the objective of transferring BCCT.core to an online format, a prototype of a web application for the automatic keypoint detection was developed and is presented in this document. Currently, the user may upload an image and automatically detect and/or manipulate its keypoints. This prototype is completely scalable and can be upgraded with new functionalities according to the user’s needs. © 2020, Springer Nature Switzerland AG.

2020

Evolution, current challenges, and future possibilities in the objective assessment of aesthetic outcome of breast cancer locoregional treatment

Authors
Cardoso, JS; Silva, W; Cardoso, MJ;

Publication
Breast

Abstract
The Breast Cancer overall survival rate has raised impressively in the last 20 years mainly due to improved screening and effectiveness of treatments. This increase in survival paralleled the awareness over the long-lasting impact of the side effects of treatments on patient quality of life, emphasizing the motto “a longer but better life for breast cancer patients”. In breast cancer more strikingly than in other cancers, besides the side effects of systemic treatments, there is the visible impact of surgery and radiotherapy on patients’ body image. This has sparked interest on the development of tools for the aesthetic evaluation of Breast Cancer locoregional treatments, which evolved from manual, subjective approaches to computerized, automated solutions. However, although studied for almost four decades, past solutions were not mature enough to become a standard. Recent advancements in machine learning have inspired trends toward deep-learning-based medical image analysis, also bringing new promises to the field of aesthetic assessment of locoregional treatments. In this paper, a review and discussion of the previous state-of-the-art methods in the field is conducted and the extracted knowledge is used to understand the evolution and current challenges. The aim of this paper is to delve into the current opportunities as well as motivate and guide future research in the aesthetic assessment of Breast Cancer locoregional treatments. © 2019 Elsevier Ltd

2020

Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls

Authors
Silva, J; Sousa, I; Cardoso, JS;

Publication
IEEE Journal of Biomedical and Health Informatics

Abstract

Supervised
thesis

2020

Performance Anomaly Detection in 802.11 Wireless Networks Applying Hidden Markov Models

Author
Anisa Allahdadidastjerdi

Institution
UP-FCUP

2020

Sign Language Recognition: Integrating Prior Domain Knowledge into Deep Neural Networks

Author
Pedro Miguel Martins Ferreira

Institution
UP-FEUP

2019

Framework for Machine Learning Interpretability Assessment

Author
Diogo Antunes Vaz de Carvalho

Institution
UP-FEUP

2019

Machine Learning Applied to Fall Prediction and Detection Using Wearable Sensors

Author
Joana Raquel Cerqueira da Silva

Institution
UP-FEUP

2019

Glaucoma in Fundus Image

Author
José Luís Pacheco Martins

Institution
UP-FEUP