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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 6500 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

018
Publicações

2023

Disentangled Representation Learning for Privacy-Preserving Case-Based Explanations

Autores
Montenegro, H; Silva, W; Cardoso, JS;

Publicação
MEDICAL APPLICATIONS WITH DISENTANGLEMENTS, MAD 2022

Abstract
The lack of interpretability of Deep Learning models hinders their deployment in clinical contexts. Case-based explanations can be used to justify these models' decisions and improve their trustworthiness. However, providing medical cases as explanations may threaten the privacy of patients. We propose a generative adversarial network to disentangle identity and medical features from images. Using this network, we can alter the identity of an image to anonymize it while preserving relevant explanatory features. As a proof of concept, we apply the proposed model to biometric and medical datasets, demonstrating its capacity to anonymize medical images while preserving explanatory evidence and a reasonable level of intelligibility. Finally, we demonstrate that the model is inherently capable of generating counterfactual explanations.

2023

Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods

Autores
Albuquerque, T; Rosado, L; Cruz, R; Vasconcelos, MJM; Oliveira, T; Cardoso, JS;

Publicação
Intelligent Systems with Applications

Abstract
Microscopic techniques in low-to-middle income countries are constrained by the lack of adequate equipment and trained operators. Since light microscopy delivers crucial methods for the diagnosis and screening of numerous diseases, several efforts have been made by the scientific community to develop low-cost devices such as 3D-printed portable microscopes. Nevertheless, these devices present some drawbacks that directly affect image quality: the capture of the samples is done via mobile phones; more affordable lenses are usually used, leading to poorer physical properties and images with lower depth of field; misalignments in the microscopic set-up regarding optical, mechanical, and illumination components are frequent, causing image distortions such as chromatic aberrations. This work investigates several pre-processing methods to tackle the presented issues and proposed a new workflow for low-cost microscopy. Additionally, two new deep learning models based on Convolutional Neural Networks are also proposed (EDoF-CNN-Fast and EDoF-CNN-Pairwise) to generate Extended Depth of Field (EDoF) images, and compared against state-of-the-art approaches. The models were tested using two different datasets of cytology microscopic images: public Cervix93 and a new dataset that has been made publicly available containing images captured with µSmartScope. Experimental results demonstrate that the proposed workflow can achieve state-of-the-art performance when generating EDoF images from low-cost microscopes. © 2022 The Author(s)

2023

Symmetry-based regularization in deep breast cancer screening

Autores
Castro, E; Pereira, JC; Cardoso, JS;

Publicação
MEDICAL IMAGE ANALYSIS

Abstract
Breast cancer is the most common and lethal form of cancer in women. Recent efforts have focused on developing accurate neural network-based computer-aided diagnosis systems for screening to help anticipate this disease. The ultimate goal is to reduce mortality and improve quality of life after treatment. Due to the difficulty in collecting and annotating data in this domain, data scarcity is - and will continue to be - a limiting factor. In this work, we present a unified view of different regularization methods that incorporate domain-known symmetries in the model. Three general strategies were followed: (i) data augmentation, (ii) invariance promotion in the loss function, and (iii) the use of equivariant architectures. Each of these strategies encodes different priors on the functions learned by the model and can be readily introduced in most settings. Empirically we show that the proposed symmetry-based regularization procedures improve generalization to unseen examples. This advantage is verified in different scenarios, datasets and model architectures. We hope that both the principle of symmetry-based regularization and the concrete methods presented can guide development towards more data-efficient methods for breast cancer screening as well as other medical imaging domains.

2023

Two-Stage Framework for Faster Semantic Segmentation

Autores
Cruz, R; Silva, DTE; Goncalves, T; Carneiro, D; Cardoso, JS;

Publicação
SENSORS

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. Conventional models spend as much effort classifying easy-to-segment pixels as they do classifying hard-to-segment pixels. This is inefficient, especially when deploying to situations with computational constraints. In this work, we propose a framework wherein the model first produces a rough segmentation of the image, and then patches of the image estimated as hard to segment are refined. The framework is evaluated in four datasets (autonomous driving and biomedical), across four state-of-the-art architectures. Our method accelerates inference time by four, with additional gains for training time, at the cost of some output quality.

2023

Deep Minutiae Fingerprint Extraction Using Equivariance Priors

Autores
Gouveia, M; Castro, E; Rebelo, A; Cardoso, JS; Patrão, B;

Publicação
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023, Volume 4: BIOSIGNALS, Lisbon, Portugal, February 16-18, 2023.

Abstract

Teses
supervisionadas

2022

Multimodal Cervical Cancer Diagnosis: Deep Learning for Automatic Decision Support

Autor
Tomé Mendes Albuquerque

Instituição
UP-FEUP

2022

Biomedical Multimodal Explanations – Increasing Diversity and Complementarity in Explainable Artificial Intelligence

Autor
Diogo Baptista Martins da Mata

Instituição
UP-FEUP

2022

Geometric Deep Learning in Fingerprint Recognition Systems

Autor
Margarida Gonçalves Gouveia

Instituição
UP-FEUP

2022

Robust occupant action classification in shared autonomous vehicles

Autor
Vítor Hugo Pereira Barbosa

Instituição
UP-FEUP

2022

A new vision in pathology: from clinical implementation of digital pathology to algorithm development in computational pathology

Autor
Diana Leitão Montezuma Pego Felizardo

Instituição
UP-ICBAS