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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

  • Name

    Luís Filipe Teixeira
  • Role

    Senior Researcher
  • Since

    17th September 2001
003
Publications

2024

Explainable Deep Learning Methods in Medical Image Classification: A Survey

Authors
Patrício, C; Neves, JC; Teixeira, LF;

Publication
ACM Comput. Surv.

Abstract

2024

Explainable Deep Learning Methods in Medical Image Classification: A Survey

Authors
Patrício, C; Neves, JC; Lincs, N; Teixeira, LF;

Publication
ACM COMPUTING SURVEYS

Abstract
The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are discussed.

2023

Deep learning-based human action recognition to leverage context awareness in collaborative assembly

Authors
Moutinho, D; Rocha, LF; Costa, CM; Teixeira, LF; Veiga, G;

Publication
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

Abstract
Human-Robot Collaboration is a critical component of Industry 4.0, contributing to a transition towards more flexible production systems that are quickly adjustable to changing production requirements. This paper aims to increase the natural collaboration level of a robotic engine assembly station by proposing a cognitive system powered by computer vision and deep learning to interpret implicit communication cues of the operator. The proposed system, which is based on a residual convolutional neural network with 34 layers and a long -short term memory recurrent neural network (ResNet-34 + LSTM), obtains assembly context through action recognition of the tasks performed by the operator. The assembly context was then integrated in a collaborative assembly plan capable of autonomously commanding the robot tasks. The proposed model showed a great performance, achieving an accuracy of 96.65% and a temporal mean intersection over union (mIoU) of 94.11% for the action recognition of the considered assembly. Moreover, a task-oriented evaluation showed that the proposed cognitive system was able to leverage the performed human action recognition to command the adequate robot actions with near-perfect accuracy. As such, the proposed system was considered as successful at increasing the natural collaboration level of the considered assembly station.

2023

GASTeN: Generative Adversarial Stress Test Networks

Authors
Cunha, L; Soares, C; Restivo, A; Teixeira, LF;

Publication
ADVANCES IN INTELLIGENT DATA ANALYSIS XXI, IDA 2023

Abstract
Concerns with the interpretability of ML models are growing as the technology is used in increasingly sensitive domains (e.g., health and public administration). Synthetic data can be used to understand models better, for instance, if the examples are generated close to the frontier between classes. However, data augmentation techniques, such as Generative Adversarial Networks (GAN), have been mostly used to generate training data that leads to better models. We propose a variation of GANs that, given a model, generates realistic data that is classified with low confidence by a given classifier. The generated examples can be used in order to gain insights on the frontier between classes. We empirically evaluate our approach on two well-known image classification benchmark datasets, MNIST and Fashion MNIST. Results show that the approach is able to generate images that are closer to the frontier when compared to the original ones, but still realistic. Manual inspection confirms that some of those images are confusing even for humans.

2023

MobileWeatherNet for LiDAR-Only Weather Estimation

Authors
da Silva, MP; Carneiro, D; Fernandes, J; Texeira, LF;

Publication
2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN

Abstract
An autonomous vehicle relying on LiDAR data should be able to assess its limitations in real time without depending on external information or additional sensors. The point cloud generated by the sensor is subjected to significant degradation under adverse weather conditions (rain, fog, and snow), which limits the vehicle's visibility and performance. With this in mind, we show that point cloud data contains sufficient information to estimate the weather accurately and present MobileWeatherNet, a LiDAR-only convolutional neural network that uses the bird's-eye view 2D projection to extract point clouds' weather condition and improves state-of-the-art performance by 15% in terms of the balanced accuracy while reducing inference time by 63%. Moreover, this paper demonstrates that among common architectures, the use of the bird's eye view significantly enhances their performance without an increase in complexity. To the extent of our knowledge, this is the first approach that uses deep learning for weather estimation using point cloud data in the form of a bird's-eye-view projection.

Supervised
thesis

2022

Using Deep Learning and Computer Vision to enhance Human-Robot Collaboration

Author
Duarte Alão Magalhães Silva Moutinho

Institution
UP-FEUP

2022

Model-Agnostic Generation Of Example-Based Explanations For The Post-Hoc Assessment Of Cervical Cytology Models

Author
Luís Henrique Condado Marques

Institution
UP-FEUP

2022

Human Action and Facial Expressions Recognition in a VR game

Author
Júlio Pinto de Castro Lopes

Institution
UP-FEUP

2022

An anatomical breast atlas: automatic segmentation of key points in multiple radiological modalities

Author
João Pedro Fonseca Teixeira

Institution
UP-FCUP

2022

GASTeN: Generative Adversarial Stress Test Networks

Author
Luís Pedro Pereira Lopes Mascarenhas Cunha

Institution
UP-FEUP