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Sobre

Sobre

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.

Tópicos
de interesse
Detalhes

Detalhes

015
Publicações

2022

Development of a Screening Method for Sulfamethoxazole in Environmental Water by Digital Colorimetry Using a Mobile Device

Autores
Peixoto, PS; Carvalho, PH; Machado, A; Barreiros, L; Bordalo, AA; Oliveira, HP; Segundo, MA;

Publicação
CHEMOSENSORS

Abstract
Antibiotic resistance is a major health concern of the 21st century. The misuse of antibiotics over the years has led to their increasing presence in the environment, particularly in water resources, which can exacerbate the transmission of resistance genes and facilitate the emergence of resistant microorganisms. The objective of the present work is to develop a chemosensor for screening of sulfonamides in environmental waters, targeting sulfamethoxazole as the model analyte. The methodology was based on the retention of sulfamethoxazole in disks containing polystyrene divinylbenzene sulfonated sorbent particles and reaction with p-dimethylaminocinnamaldehyde, followed by colorimetric detection using a computer-vision algorithm. Several color spaces (RGB, HSV and CIELAB) were evaluated, with the coordinate a_star, from the CIELAB color space, providing the highest sensitivity. Moreover, in order to avoid possible errors due to variations in illumination, a color palette is included in the picture of the analytical disk, and a correction using the a_star value from one of the color patches is proposed. The methodology presented recoveries of 82–101% at 0.1 µg and 0.5 µg of sulfamethoxazole (25 mL), providing a detection limit of 0.08 µg and a quantification limit of 0.26 µg. As a proof of concept, application to in-field analysis was successfully implemented.

2022

Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset

Autores
Sousa, J; Pereira, T; Silva, F; Silva, MC; Vilares, AT; Cunha, A; Oliveira, HP;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 ± 0.2140 and 0.8750 ± 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.

2022

Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

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

Publicação
JOURNAL OF PERSONALIZED MEDICINE

Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.

2022

Differential Gene Expression Analysis of the Most Relevant Genes for Lung Cancer Prediction and Sub-type Classification

Autores
Ramos, B; Pereira, T; Silva, F; Costa, JL; Oliveira, HP;

Publicação
Pattern Recognition and Image Analysis - 10th Iberian Conference, IbPRIA 2022, Aveiro, Portugal, May 4-6, 2022, Proceedings

Abstract

2022

An Edge-Based Computer Vision Approach for Determination of Sulfonamides in Water

Autores
Rocha, I; Azevedo, F; Carvalho, PH; Peixoto, PS; Segundo, MA; Oliveira, HP;

Publicação
Pattern Recognition and Image Analysis - 10th Iberian Conference, IbPRIA 2022, Aveiro, Portugal, May 4-6, 2022, Proceedings

Abstract

Teses
supervisionadas

2021

University Entrepreneurship: The Process of Knowledge-based Value Creation.

Autor
Sara Lúcia Correia Neves

Instituição
UP-FEP

2021

Automatic processing of images of chromotropic traps for identification and quantification of Trioza erytreae and Scaphoideus titanus

Autor
Beatriz Lobo da Silva Pinto Bessa

Instituição
UP-FCUP

2021

Improving the Management of Future Distribution Networks by Leveraging TSO-DSO Coordination Schemes

Autor
Micael Filipe de Oliveira Simões

Instituição
UP-FEUP

2021

Estimation of tissue’s optical properties from reflectance data using machine learning models

Autor
Luís Emanuel Pereira Pinto Fernandes

Instituição
UP-FEUP

2020

Al-Based Predictive Models for Personalising Lung Cancer Treatments

Autor
Joana Patrícia Machado Morgado

Instituição
UP-FCUP