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About

Hélder P. Oliveira Hélder P. Oliveira was born in Porto, Portugal, in 1980. He graduated in Electrical and Computer Engineering in 2004, received the M.Sc. degree in Automation, Instrumentation and Control in 2008 and the Ph.D. degree in Electrical and Computer Engineering in 2013 at the Faculty of Engineering of the University of Porto (FEUP), Portugal. He is currently working as Senior Researcher at INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, a R&D institute affiliated to the University of Porto. He is the Leader of the Visual Computing and Machine Intelligence Area, member of the coordination council of the Centre for Telecommunications and Multimedia, and takes part of the Breast Research Group. He is also one of the coordinators of the Data Science Hub at INESC TEC. He is also working at the Computer Science Department of the Faculty of Sciences of the University of Porto as an Invited Assistant Professor. Between 2014 and 2016 he was contracted as Invited Assistant Professor at Informatics Engineering Department of FEUP. Previously between 2008 and 2011 was working as Invited Assistant in the same Faculty and Department. Hélder Oliveira is the principal investigator in 2 funded research projects (LuCaS, MICOS), project member in 4 projects (S-MODE, HEMOSwimmers, LEGEM and TAMI). In the past was also project member in 5 other funded projects (one European and 4 National) and 3 other as research assistant. He was also responsible at INESC TEC for other 2 projects related with technological transfer with industry, the project Evo3DModel with Adapttech - Adaptation Technologies and the project FollicleCounter with Saúde Viável. He was the founder member and coordinator (between 2010 and 2013) of the Bio-related Image Processing and Analysis Student’s Group (BioStar) at FEUP. Since 2007 I have co-authored 20 peer-reviewed papers and 8 journal abstracts. I have 1 patent conceded (Europe, China, Japan), 3 book chapters and also 64 works in international conferences, 40 articles in national refereed conferences and participated in the creation of 3 public datasets. In total, these publications have attracted 748 citations, with h-index of 14 according to Harzing’s Publish or Perish application on March 30, 2021. He was one of the mentors and belonged to the organizer committee of the VISion Understanding and Machine Intelligence (VISUM) summer school in 6 editions of the event. He also participated in the organization of other 12 events and was invited as keynote speaker in 3 international events. Hélder Oliveira is currently supervising 6 PhD Students, and has 1 Phd Student concluded as supervisor in 2018. During his career supervised (or co-supervised) 56 MSc students. Currently supervises 4 research fellows in projects at INESC TEC. Hélder Oliveira participated as principal jury in 2 PhD and 15 MSc defences as principal examiner. Hélder Oliveira is member of Portuguese Association of Pattern Recognition (APRP) and was been elected for president of the fiscal council in 2017. His research interests include medical image analysis, bio-image analysis, computer vision, image and video processing, machine learning, data science, computer science, programming, and 3D modelling.

Interest
Topics
Details

Details

013
Publications

2021

Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images

Authors
Pereira, T; Freitas, C; Costa, JL; Morgado, J; Silva, F; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Cunha, A; Oliveira, HP;

Publication
Journal of Clinical Medicine

Abstract
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.

2021

Embedding Anatomical Characteristics in 3D Models of Lower-limb Sockets through Statistical Shape Modelling

Authors
Costa, A; Rodrigues, D; Castro, M; Assis, S; Oliveira, HP;

Publication
Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

Abstract

2021

Machine Learning and Feature Selection Methods for EGFR Mutation Status Prediction in Lung Cancer

Authors
Morgado, J; Pereira, T; Silva, F; Freitas, C; Negrao, E; de Lima, BF; da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Costa, JL; Cunha, A; Oliveira, HP;

Publication
Applied Sciences

Abstract
The evolution of personalized medicine has changed the therapeutic strategy from classical chemotherapy and radiotherapy to a genetic modification targeted therapy, and although biopsy is the traditional method to genetically characterize lung cancer tumor, it is an invasive and painful procedure for the patient. Nodule image features extracted from computed tomography (CT) scans have been used to create machine learning models that predict gene mutation status in a noninvasive, fast, and easy-to-use manner. However, recent studies have shown that radiomic features extracted from an extended region of interest (ROI) beyond the tumor, might be more relevant to predict the mutation status in lung cancer, and consequently may be used to significantly decrease the mortality rate of patients battling this condition. In this work, we investigated the relation between image phenotypes and the mutation status of Epidermal Growth Factor Receptor (EGFR), the most frequently mutated gene in lung cancer with several approved targeted-therapies, using radiomic features extracted from the lung containing the nodule. A variety of linear, nonlinear, and ensemble predictive classification models, along with several feature selection methods, were used to classify the binary outcome of wild-type or mutant EGFR mutation status. The results show that a comprehensive approach using a ROI that included the lung with nodule can capture relevant information and successfully predict the EGFR mutation status with increased performance compared to local nodule analyses. Linear Support Vector Machine, Elastic Net, and Logistic Regression, combined with the Principal Component Analysis feature selection method implemented with 70% of variance in the feature set, were the best-performing classifiers, reaching Area Under the Curve (AUC) values ranging from 0.725 to 0.737. This approach that exploits a holistic analysis indicates that information from more extensive regions of the lung containing the nodule allows a more complete lung cancer characterization and should be considered in future radiogenomic studies.

2021

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Authors
Silva, F; Pereira, T; Morgado, J; Frade, J; Mendes, J; Freitas, C; Negrao, E; De Lima, BF; Da Silva, MC; Madureira, AJ; Ramos, I; Hespanhol, V; Costa, JL; Cunha, A; Oliveira, HP;

Publication
IEEE Access

Abstract
Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification. CCBYNCND

2021

The Role of Liquid Biopsy in Early Diagnosis of Lung Cancer

Authors
Freitas, C; Sousa, C; Machado, F; Serino, M; Santos, V; Cruz Martins, N; Teixeira, A; Cunha, A; Pereira, T; Oliveira, HP; Costa, JL; Hespanhol, V;

Publication
Frontiers in Oncology

Abstract
Liquid biopsy is an emerging technology with a potential role in the screening and early detection of lung cancer. Several liquid biopsy-derived biomarkers have been identified and are currently under ongoing investigation. In this article, we review the available data on the use of circulating biomarkers for the early detection of lung cancer, focusing on the circulating tumor cells, circulating cell-free DNA, circulating micro-RNAs, tumor-derived exosomes, and tumor-educated platelets, providing an overview of future potential applicability in the clinical practice. While several biomarkers have shown exciting results, diagnostic performance and clinical applicability is still limited. The combination of different biomarkers, as well as their combination with other diagnostic tools show great promise, although further research is still required to define and validate the role of liquid biopsies in clinical practice.

Supervised
thesis

2020

Lung computed tomography image synthesis using generative adversarial networks

Author
José Miguel Ferreira Mendes

Institution
INESCTEC

2020

Personalized 3D Breast Cancer Models: from Multimodal Registration to Predictive Shape Modelling

Author
Sílvia da Conceição Neto Bessa

Institution
INESCTEC

2020

Al-Based Predictive Models for Personalising Lung Cancer Treatments

Author
Joana Patrícia Machado Morgado

Institution
INESCTEC

2020

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

Author
João Pedro Fonseca Teixeira

Institution
INESCTEC

2020

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

Author
João Pedro da Silva Monteiro

Institution
INESCTEC