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About

I hold an integrated masters (bachelor+master) degree in Bioengineering, branch of Biomedical Engineering, focused on a wide array of subjects related to healthcare and engineering, such as sensors and electronics, signal and image processing, computer aided diagnosis, medical instruments, robotics and bionics, mobile programming, and software engineering.

I am currently a Research Assistant at CTM - INESC TEC and a PhD student at FEUP, conducting research in signal and image analysis, combined with machine learning for pattern recognition applications. My work focuses mainly on biometrics, for human identification and authentication, using electrocardiographic signals from off-the-person, highly noisy, unconstrained, and seamless acquisition settings.

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Publications

2020

Secure Triplet Loss for End-to-End Deep Biometrics

Authors
Pinto, JR; Cardoso, JS; Correia, MV;

Publication
2020 8th International Workshop on Biometrics and Forensics (IWBF)

Abstract

2020

Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

Authors
Oliveira, SP; Pinto, JR; Goncalves, T; Canas Marques, R; Cardoso, MJ; Oliveira, HP; Cardoso, JS;

Publication
Applied Sciences

Abstract
Human epidermal growth factor receptor 2 (HER2) evaluation commonly requires immunohistochemistry (IHC) tests on breast cancer tissue, in addition to the standard haematoxylin and eosin (H&E) staining tests. Additional costs and time spent on further testing might be avoided if HER2 overexpression could be effectively inferred from H&E stained slides, as a preliminary indication of the IHC result. In this paper, we propose the first method that aims to achieve this goal. The proposed method is based on multiple instance learning (MIL), using a convolutional neural network (CNN) that separately processes H&E stained slide tiles and outputs an IHC label. This CNN is pretrained on IHC stained slide tiles but does not use these data during inference/testing. H&E tiles are extracted from invasive tumour areas segmented with the HASHI algorithm. The individual tile labels are then combined to obtain a single label for the whole slide. The network was trained on slides from the HER2 Scoring Contest dataset (HER2SC) and tested on two disjoint subsets of slides from the HER2SC database and the TCGA-TCIA-BRCA (BRCA) collection. The proposed method attained 83.3 % classification accuracy on the HER2SC test set and 53.8 % on the BRCA test set. Although further efforts should be devoted to achieving improved performance, the obtained results are promising, suggesting that it is possible to perform HER2 overexpression classification on H&E stained tissue slides.

2020

Interpretable Biometrics: Should We Rethink How Presentation Attack Detection is Evaluated?

Authors
Sequeira, AF; Silva, W; Pinto, JR; Gonçalves, T; Cardoso, JS;

Publication
8th International Workshop on Biometrics and Forensics, IWBF 2020, Porto, Portugal, April 29-30, 2020

Abstract
Presentation attack detection (PAD) methods are commonly evaluated using metrics based on the predicted labels. This is a limitation, especially for more elusive methods based on deep learning which can freely learn the most suitable features. Though often being more accurate, these models operate as complex black boxes which makes the inner processes that sustain their predictions still baffling. Interpretability tools are now being used to delve deeper into the operation of machine learning methods, especially artificial networks, to better understand how they reach their decisions. In this paper, we make a case for the integration of interpretability tools in the evaluation of PAD. A simple model for face PAD, based on convolutional neural networks, was implemented and evaluated using both traditional metrics (APCER, BPCER and EER) and interpretability tools (Grad-CAM), using data from the ROSE Youtu video collection. The results show that interpretability tools can capture more completely the intricate behavior of the implemented model, and enable the identification of certain properties that should be verified by a PAD method that is robust, coherent, meaningful, and can adequately generalize to unseen data and attacks. One can conclude that, with further efforts devoted towards higher objectivity in interpretability, this can be the key to obtain deeper and more thorough PAD performance evaluation setups. © 2020 IEEE.

2020

Self-Learning with Stochastic Triplet Loss

Authors
Pinto, JR; Cardoso, JS;

Publication
2020 International Joint Conference on Neural Networks (IJCNN)

Abstract

2020

Explaining ECG Biometrics: Is It All In The QRS?

Authors
Pinto, JR; Cardoso, JS;

Publication
BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, online, 16.-18. September 2020.

Abstract