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Sobre

Sobre

Jaime S. Cardoso, licenciado em Engenharia e Eletrotécnica e de Computadores em 1999, Mestre em Engenharia Matemática em 2005 e doutorado em Visão Computacional em 2006, todos pela Universidade do Porto. Professor Associado com agregação na Faculdade de Engenharia da Universidade do Porto (FEUP) e Investigador Sénior em 'Information Processing and Pattern Recognition' no Centro de Telecomunicações e Multimédia do INESC TEC.

A sua investigação assenta em três grandes domínios: visão computacional, "machine learning" e sistemas de suporte à decisão. A investigação em processamento de imagem e vídeo tem abordado a área de biometria, imagem médica e "video tracking" para aplicações de vigilância e desportos. O trabalho em "machine learning" foca-se na adaptação de sistemas de aprendizagem às condições desafiantes de informação visual. A ênfase dos sistemas de suporte à decisão tem sido dirigida a aplicações médicas, sempre ancoradas com a análise automática de informação visual.

É co-autor de mais de 150 artigos, dos quais mais de 50 em jornais internacionais, com mais de 2400 citações (google scholar). Foi investigador principal em 6 projectos de I&D e participou em 14 projectos de I&D, incluindo 5 projectos europeus e um contrato directo com a BBC do Reino Unido.

Tópicos
de interesse
Detalhes

Detalhes

008
Publicações

2018

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

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

Publicação
Sensors

Abstract

2018

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

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

Publicação
EUROPEAN JOURNAL OF CANCER

Abstract

2018

A deep learning approach for the forensic evaluation of sexual assault

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

Publicação
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

Autores
Fernandes, K; Cardoso, JS; Fernandes, J;

Publicação
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

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

Publicação
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.

Teses
supervisionadas

2017

Rotated Filters and Learning Strategies in Convolutional Neural Networks for Mammographic Lesions Detection

Autor
Eduardo Meca Castro

Instituição
UP-FEUP

2017

Continuous Biometric Identification on the Steering Wheel

Autor
João Tiago Ribeiro Pinto

Instituição
UP-FEUP

2017

Automatic Assessment of Infantile Hemangiomas

Autor
Pedro Guilherme Reis Alves

Instituição
UP-FEUP

2017

Learning in evolving video streams

Autor
Samaneh Khoshrou

Instituição
UP-FEUP

2017

Reinforcement Learning for Primary Care Appointment Scheduling

Autor
Tiago Salgado Magalhães Taveira-Gomes

Instituição
UP-FEUP