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About

Wilson Silva holds an integrated master (BSc+MSc) degree in Electrical and Computer Engineering obtained from the Faculty of Engineering of the University of Porto (FEUP) in 2016. During the master, he was also a visiting student at the Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany. Since the end of 2017, Wilson is a PhD student in Electrical and Computer Engineering at FEUP and a Research Assistant at INESC TEC, where he is associated with the Visual Computing and Machine Intelligence (VCMI) and Breast Research groups. In between these academic and research experiences, he worked for one year as an IT Advisor at KPMG Portugal in Lisbon. During the academic year of 2018/2019, Wilson was an Invited Assistant at FEUP, teaching practical classes of introductory courses in programming and digital systems. Currently, he is a visiting PhD student at the Bern University Hospital (Inselspital) and at the University of Bern, in Bern, Switzerland. His main research interests include Machine Learning and Computer Vision, with a particular focus on Explainable Artificial Intelligence and Medical Image Analysis.

Interest
Topics
Details

Details

003
Publications

2022

Privacy-Preserving Case-Based Explanations: Enabling Visual Interpretability by Protecting Privacy

Authors
Montenegro, H; Silva, W; Gaudio, A; Fredrikson, M; Smailagic, A; Cardoso, JS;

Publication
IEEE ACCESS

Abstract

2021

Privacy-Preserving Generative Adversarial Network for Case-Based Explainability in Medical Image Analysis

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

Publication
IEEE ACCESS

Abstract

2021

An exploratory study of interpretability for face presentation attack detection

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

Publication
IET BIOMETRICS

Abstract
Biometric recognition and presentation attack detection (PAD) methods strongly rely on deep learning algorithms. Though often more accurate, these models operate as complex black boxes. Interpretability tools are now being used to delve deeper into the operation of these methods, which is why this work advocates their integration in the PAD scenario. Building upon previous work, a face PAD model based on convolutional neural networks was implemented and evaluated both through traditional PAD metrics and with interpretability tools. An evaluation on the stability of the explanations obtained from testing models with attacks known and unknown in the learning step is made. To overcome the limitations of direct comparison, a suitable representation of the explanations is constructed to quantify how much two explanations differ from each other. From the point of view of interpretability, the results obtained in intra and inter class comparisons led to the conclusion that the presence of more attacks during training has a positive effect in the generalisation and robustness of the models. This is an exploratory study that confirms the urge to establish new approaches in biometrics that incorporate interpretability tools. Moreover, there is a need for methodologies to assess and compare the quality of explanations.

2020

Deep Aesthetic Assessment of Breast Cancer Surgery Outcomes

Authors
Goncalves, T; Silva, W; Cardoso, J;

Publication
XV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING - MEDICON 2019

Abstract
Breast cancer is a highly mutable and rapidly evolving disease, with a large worldwide incidence. Even though, it is estimated that approximately 90% of the cases are treatable and curable if detected on early staging and given the best treatment. Nowadays, with the existence of breast cancer routine screening habits, better clinical treatment plans and proper management of the disease, it is possible to treat most cancers with conservative approaches, also known as breast cancer conservative treatments (BCCT). With such a treatment methodology, it is possible to focus on the aesthetic results of the surgery and the patient’s Quality of Life, which may influence BCCT outcomes. In the past, this assessment would be done through subjective methods, where a panel of experts would be needed to perform the assessment; however, with the development of computer vision techniques, objective methods, such as BAT© and BCCT.core, which perform the assessment based on asymmetry measurements, have been used. On the other hand, they still require information given by the user and none of them has been considered the gold standard for this task. Recently, with the advent of deep learning techniques, algorithms capable of improving the performance of traditional methods on the detection of breast fiducial points (required for asymmetry measurements) have been proposed and showed promising results. There is still, however, a large margin for investigation on how to integrate such algorithms in a complete application, capable of performing an end-to-end classification of the BCCT outcomes. Taking this into account, this thesis shows a comparative study between deep convolutional networks for image segmentation and two different quality-driven keypoint detection architectures for the detection of the breast contour. One that uses a deep learning model that has learned to predict the quality (given by the mean squared error) of an array of keypoints, and, based on this quality, applies the backpropagation algorithm, with gradient descent, to improve them; another which uses a deep learning model which was trained with the quality as a regularization method and that used iterative refinement, in each training step, to improve the quality of the keypoints that were fed into the network. Although none of the methods surpasses the current state of the art, they present promising results for the creation of alternative methodologies to address other regression problems in which the learning of the quality metric may be easier. Following the current trend in the field of web development and with the objective of transferring BCCT.core to an online format, a prototype of a web application for the automatic keypoint detection was developed and is presented in this document. Currently, the user may upload an image and automatically detect and/or manipulate its keypoints. This prototype is completely scalable and can be upgraded with new functionalities according to the user’s needs. © 2020, Springer Nature Switzerland AG.

2020

Evolution, current challenges, and future possibilities in the objective assessment of aesthetic outcome of breast cancer locoregional treatment

Authors
Cardoso, JS; Silva, W; Cardoso, MJ;

Publication
BREAST

Abstract
The Breast Cancer overall survival rate has raised impressively in the last 20 years mainly due to improved screening and effectiveness of treatments. This increase in survival paralleled the awareness over the long-lasting impact of the side effects of treatments on patient quality of life, emphasizing the motto “a longer but better life for breast cancer patients”. In breast cancer more strikingly than in other cancers, besides the side effects of systemic treatments, there is the visible impact of surgery and radiotherapy on patients’ body image. This has sparked interest on the development of tools for the aesthetic evaluation of Breast Cancer locoregional treatments, which evolved from manual, subjective approaches to computerized, automated solutions. However, although studied for almost four decades, past solutions were not mature enough to become a standard. Recent advancements in machine learning have inspired trends toward deep-learning-based medical image analysis, also bringing new promises to the field of aesthetic assessment of locoregional treatments. In this paper, a review and discussion of the previous state-of-the-art methods in the field is conducted and the extracted knowledge is used to understand the evolution and current challenges. The aim of this paper is to delve into the current opportunities as well as motivate and guide future research in the aesthetic assessment of Breast Cancer locoregional treatments. © 2019 Elsevier Ltd

Supervised
thesis

2021

A privacy-preserving framework for case-based interpretability in machine learning

Author
Maria Helena Sampaio de Mendonça Montenegro e Almeida

Institution
UP-FEUP