Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About
Download Photo HD

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

2020

Automatic detection of perforators for microsurgical reconstruction

Authors
Mavioso, C; Araujo, RJ; Oliveira, HP; Anacleto, JC; Vasconcelos, MA; Pinto, D; Gouveia, PF; Alves, C; Cardoso, F; Cardoso, JS; Cardoso, MJ;

Publication
The Breast

Abstract

2020

Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS

Authors
Pinheiro, G; Pereira, T; Dias, C; Freitas, C; Hespanhol, V; Costa, JL; Cunha, A; Oliveira, HP;

Publication
Scientific Reports

Abstract

2020

3D Digital Breast Cancer Models with Multimodal Fusion Algorithms

Authors
Bessa, S; Gouveia, PF; Carvalho, PH; Rodrigues, C; Silva, NL; Cardoso, F; Cardoso, JS; Oliveira, HP; Cardoso, MJ;

Publication
The Breast

Abstract

Supervised
thesis

2020

3D Generative Models for lower limb sockets

Author
Ana Luísa Pereira da Costa

Institution
INESCTEC

2020

Android Application for Determination of sulfonamides in water using digital image colorimetry

Author
Pedro Daniel dos Santos Reis

Institution
INESCTEC

2020

Multiple instance learning for lung cancer characterization in computed tomography scans

Author
Julieta Pintado Jorge Frade

Institution
INESCTEC

2020

Active Learning for Abnormalities Detection on Videos of Endoscopic Capsules

Author
Maria Inês Fernandes Xavier

Institution
INESCTEC

2020

Estimate the emotional state of a user in physical rehabilitation

Author
Diogo Ricardo Castro Gonçalves

Institution
INESCTEC