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About

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

002
Publications

2023

Fill in the blank for fashion complementary outfit product Retrieval: VISUM summer school competition

Authors
Castro, E; Ferreira, PM; Rebelo, A; Rio-Torto, I; Capozzi, L; Ferreira, MF; Goncalves, T; Albuquerque, T; Silva, W; Afonso, C; Sousa, RG; Cimarelli, C; Daoudi, N; Moreira, G; Yang, HY; Hrga, I; Ahmad, J; Keswani, M; Beco, S;

Publication
MACHINE VISION AND APPLICATIONS

Abstract
Every year, the VISion Understanding and Machine intelligence (VISUM) summer school runs a competition where participants can learn and share knowledge about Computer Vision and Machine Learning in a vibrant environment. 2021 VISUM's focused on applying those methodologies in fashion. Recently, there has been an increase of interest within the scientific community in applying computer vision methodologies to the fashion domain. That is highly motivated by fashion being one of the world's largest industries presenting a rapid development in e-commerce mainly since the COVID-19 pandemic. Computer Vision for Fashion enables a wide range of innovations, from personalized recommendations to outfit matching. The competition enabled students to apply the knowledge acquired in the summer school to a real-world problem. The ambition was to foster research and development in fashion outfit complementary product retrieval by leveraging vast visual and textual data with domain knowledge. For this, a new fashion outfit dataset (acquired and curated by FARFETCH) for research and benchmark purposes is introduced. Additionally, a competitive baseline with an original negative sampling process for triplet mining was implemented and served as a starting point for participants. The top 3 performing methods are described in this paper since they constitute the reference state-of-the-art for this particular problem. To our knowledge, this is the first challenge in fashion outfit complementary product retrieval. Moreover, this joint project between academia and industry brings several relevant contributions to disseminating science and technology, promoting economic and social development, and helping to connect early-career researchers to real-world industry challenges.

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

2022

Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes

Authors
Silva, W; Carvalho, M; Mavioso, C; Cardoso, MJ; Cardoso, JS;

Publication
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2022)

Abstract
Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80% of patients having a 10-year survival period. Given the serious that impact breast cancer treatments can have on a patient's body image, consequently affecting her self-confidence and sexual and intimate relationships, it is paramount to ensure that women receive the treatment that optimizes both survival and aesthetic outcomes. Currently, there is no gold standard for evaluating the aesthetic outcome of breast cancer treatment. In addition, there is no standard way to show patients the potential outcome of surgery. The presentation of similar cases from the past would be extremely important to manage women's expectations of the possible outcome. In this work, we propose a deep neural network to perform the aesthetic evaluation. As a proof-of-concept, we focus on a binary aesthetic evaluation. Besides its use for classification, this deep neural network can also be used to find the most similar past cases by searching for nearest neighbours in the high-semantic space before classification. We performed the experiments on a dataset consisting of 143 photos of women after conservative treatment for breast cancer. The results for accuracy and balanced accuracy showed the superior performance of our proposed model compared to the state of the art in aesthetic evaluation of breast cancer treatments. In addition, the model showed a good ability to retrieve similar previous cases, with the retrieved cases having the same or adjacent class (in the 4-class setting) and having similar types of asymmetry. Finally, a qualitative interpretability assessment was also performed to analyse the robustness and trustworthiness of the model.

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.

Supervised
thesis

2022

Towards Biometrically-Morphed Medical Case-based Explanations

Author
Maria Manuel Domingos Carvalho

Institution
UP-FEUP

2022

Biomedical Multimodal Explanations – Increasing Diversity and Complementarity in Explainable Artificial Intelligence

Author
Diogo Baptista Martins da Mata

Institution
UP-FEUP

2021

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

Author
Maria Helena Sampaio de Mendonça Montenegro e Almeida

Institution
UP-FEUP