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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.

Interest
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

2021

Incremental Learning for Dermatological Imaging Modality Classification

Authors
Morgado, AC; Andrade, C; Teixeira, LF; Vasconcelos, MJM;

Publication
JOURNAL OF IMAGING

Abstract
With the increasing adoption of teledermatology, there is a need to improve the automatic organization of medical records, being dermatological image modality a key filter in this process. Although there has been considerable effort in the classification of medical imaging modalities, this has not been in the field of dermatology. Moreover, as various devices are used in teledermatological consultations, image acquisition conditions may differ. In this work, two models (VGG-16 and MobileNetV2) were used to classify dermatological images from the Portuguese National Health System according to their modality. Afterwards, four incremental learning strategies were applied to these models, namely naive, elastic weight consolidation, averaged gradient episodic memory, and experience replay, enabling their adaptation to new conditions while preserving previously acquired knowledge. The evaluation considered catastrophic forgetting, accuracy, and computational cost. The MobileNetV2 trained with the experience replay strategy, with 500 images in memory, achieved a global accuracy of 86.04% with only 0.0344 of forgetting, which is 6.98% less than the second-best strategy. Regarding efficiency, this strategy took 56 s per epoch longer than the baseline and required, on average, 4554 megabytes of RAM during training. Promising results were achieved, proving the effectiveness of the proposed approach.

2021

Improving Automatic Quality Inspection in the Automotive Industry by Combining Simulated and Real Data

Authors
Pinho, P; Rio Torto, I; Teixeira, LF;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Considerable amounts of data are required for a deep learning model to generalize to unseen cases successfully. Furthermore, such data is often manually labeled, making its annotation process costly and time-consuming. We propose using unlabeled real-world data in conjunction with automatically labeled synthetic data, obtained from simulators, to surpass the increasing need for annotated data. By obtaining real counterparts of simulated samples using CycleGAN and subsequently performing fine-tuning with such samples, we manage to improve a vehicle part’s detection system performance by 2.5%, compared to the baseline exclusively trained on simulated images. We explore adding a semantic consistency loss to CycleGAN by re-utilizing previous work’s trained networks to regularize the conversion process. Moreover, the addition of a post-processing step, which we denominate global NMS, highlights our approach’s effectiveness by better utilizing our detection model’s predictions and ultimately improving the system’s performance by 14.7%. © 2021, Springer Nature Switzerland AG.

Supervised
thesis

2021

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

Author
João Pedro da Silva Monteiro

Institution
UP-FEUP

2021

Detection of Epilepsy in EEGs using sequence models

Author
Miguel Filipe Saraiva Marques

Institution
UP-FEUP

2021

Section Detection in an Automatic Pipeline for Mapping Brain Activation in the Mouse

Author
Pedro Gonçalves Neto

Institution
UP-FEUP

2021

Combining machine learning and deep learning approaches to detect cervical cancer in cytology images

Author
Eduardo Luís Pinheiro da Silva

Institution
UP-FEUP

2021

Combining simulated and real images in deep learning

Author
Pedro Xavier Tavares Monteiro Correia de Pinho

Institution
UP-FEUP