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About

About

Luis F. Teixeira holds a Ph.D. in Electrical and Computer Engineering from Universidade do Porto in the area of computer vision (2009). Currently he is an Assistant Professor at the Department of Informatics Engineering, Faculdade de Engenharia da Universidade do Porto, and a researcher at INESC TEC. Previously he was a researcher at INESC Porto (2001-2008), Visiting Researcher at the University of Victoria (2006), and Senior Scientist at Fraunhofer AICOS (2008-2013). His current research interest include: computer vision, machine learning and interactive systems.

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Topics
Details

Details

001
Publications

2021

Data Augmentation Using Adversarial Image-to-Image Translation for the Segmentation of Mobile-Acquired Dermatological Images

Authors
Andrade, C; Teixeira, LF; Vasconcelos, MJM; Rosado, L;

Publication
Journal of Imaging

Abstract
Dermoscopic images allow the detailed examination of subsurface characteristics of the skin, which led to creating several substantial databases of diverse skin lesions. However, the dermoscope is not an easily accessible tool in some regions. A less expensive alternative could be acquiring medium resolution clinical macroscopic images of skin lesions. However, the limited volume of macroscopic images available, especially mobile-acquired, hinders developing a clinical mobile-based deep learning approach. In this work, we present a technique to efficiently utilize the sizable number of dermoscopic images to improve the segmentation capacity of macroscopic skin lesion images. A Cycle-Consistent Adversarial Network is used to translate the image between the two distinct domains created by the different image acquisition devices. A visual inspection was performed on several databases for qualitative evaluation of the results, based on the disappearance and appearance of intrinsic dermoscopic and macroscopic features. Moreover, the Fréchet Inception Distance was used as a quantitative metric. The quantitative segmentation results are demonstrated on the available macroscopic segmentation databases, SMARTSKINS and Dermofit Image Library, yielding test set thresholded Jaccard Index of 85.13% and 74.30%. These results establish a new state-of-the-art performance in the SMARTSKINS database.

2021

Adversarial Data Augmentation on Breast MRI Segmentation

Authors
Teixeira, JF; Dias, M; Batista, E; Costa, J; Teixeira, LF; Oliveira, HP;

Publication
APPLIED SCIENCES-BASEL

Abstract
The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator's architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

2021

Automatic quality inspection in the automotive industry: a hierarchical approach using simulated data

Authors
Rio-Torto, I; Campanico, AT; Pereira, A; Teixeira, LF; Filipe, V;

Publication
2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA)

Abstract

2020

Understanding the Impact of Artificial Intelligence on Services

Authors
Ferreira, P; Teixeira, JG; Teixeira, LF;

Publication
Lecture Notes in Business Information Processing

Abstract
Services are the backbone of modern economies and are increasingly supported by technology. Meanwhile, there is an accelerated growth of new technologies that are able to learn from themselves, providing more and more relevant results, i.e. Artificial Intelligence (AI). While there have been significant advances on the capabilities of AI, the impacts of this technology on service provision are still unknown. Conceptual research claims that AI offers a way to augment human capabilities or position it as a threat to human jobs. The objective of this study is to better understand the impact of AI on service, namely by understanding current trends in AI, and how they are, and will, impact service provision. To achieve this, a qualitative study, following Grounded Theory methodology was performed, with ten Artificial Intelligence experts selected from industry and academia. © Springer Nature Switzerland AG 2020.

2020

Deep Learning for Interictal Epileptiform Discharge Detection from Scalp EEG Recordings

Authors
Lourenco, C; Tjepkema Cloostermans, MC; Teixeira, LF; van Putten, MJAM;

Publication
IFMBE Proceedings

Abstract
Interictal Epileptiform Discharge (IED) detection in EEG signals is widely used in the diagnosis of epilepsy. Visual analysis of EEGs by experts remains the gold standard, outperforming current computer algorithms. Deep learning methods can be an automated way to perform this task. We trained a VGG network using 2-s EEG epochs from patients with focal and generalized epilepsy (39 and 40 patients, respectively, 1977 epochs total) and 53 normal controls (110770 epochs). Five-fold cross-validation was performed on the training set. Model performance was assessed on an independent set (734 IEDs from 20 patients with focal and generalized epilepsy and 23040 normal epochs from 14 controls). Network visualization techniques (filter visualization and occlusion) were applied. The VGG yielded an Area Under the ROC Curve (AUC) of 0.96 (95% Confidence Interval (CI) = 0.95 - 0.97). At 99% specificity, the sensitivity was 79% and only one sample was misclassified per two minutes of analyzed EEG. Filter visualization showed that filters from higher level layers display patches of activity indicative of IED detection. Occlusion showed that the model correctly identified IED shapes. We show that deep neural networks can reliably identify IEDs, which may lead to a fundamental shift in clinical EEG analysis. © 2020, Springer Nature Switzerland AG.

Supervised
thesis

2020

A Multi-modal Approach for Breast Imaging Analysis and Surgery Planning

Author
João Pedro Fonseca Teixeira

Institution
INESCTEC

2020

Fish and marine renewable’s structures deep segmentation using simulated data

Author
Paulo Sérgio da Silva Babo

Institution
UP-FEUP

2020

Unconstrained Human Pose Estimation to Support Breast Cancer Survivor's Prospective Surveillance

Author
João Pedro da Silva Monteiro

Institution
INESCTEC

2020

Software library for stream-based recommender systems

Author
Fernando André Bezerra Moura Fernandes

Institution
UP-FEUP

2020

Extracção de Informação dos Boletins de Saúde Infantil e Juvenil

Author
David Rafael Silva Falcão

Institution
UP-FEUP