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About

About

Sara Oliveira was born in Coimbra, Portugal, in 1992.

She received the MSc. Degree in Biomedical Engineering from Faculty of Sciences and Technology of University of Coimbra, Portugal, in 2016. Since 2016 she has been working as Researcher at INESC TEC, a R&D institute affiliated to the University of Porto, in the Visual Computing and Machine Intelligence Group (VCMI) and in the Breast Research Group. Currently, she is a project member in a funded research project, BCCT.plan, related with the planning of Breast Conserving Treatment. She is also enrolled in the Doctoral Program in Electrical and Computer Engineering (PDEEC), at Faculty of Engineering of University of Porto.

Her main research interests include computer vision, image processing, medical imaging, 3D modelling, machine learning and artificial intelligence.

Interest
Topics
Details

Details

001
Publications

2018

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery

Authors
Zolfagharnasab, H; Bessa, S; Oliveira, SP; Faria, P; Teixeira, JF; Cardoso, JS; Oliveira, HP;

Publication
Sensors

Abstract

2017

Segmentation of Eye Fundus Images by density clustering in diabetic retinopathy

Authors
Furtado, P; Travassos, C; Monteiro, R; Oliveira, S; Baptista, C; Carrilho, F;

Publication
2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

Abstract
Early diagnosis is crucial in Diabetic Retinopathy (DR), to avoid further complications. The disease can be classified into one of two stages (an early stage of non-proliferative and a later stage of proliferative diabetic retinopathy), diagnosed based on existence and quantity of a characteristic set of lesions, such as micro-aneurysms, hemorrhages or exudates, in Eye Fundus Images (EFI). It is therefore important to segment adequately regions of potential lesions, to highlight and classify the lesions and the degree of DR. Density clustering methods are promising candidates to isolate individual lesions, and should be used together with effective techniques for vascular tree removal, feature extraction and classification. In this work we report on our approach, results, tradeoffs and conclusions for segmenting and detecting individual lesions. © 2017 IEEE.

Supervised
thesis

2017

Specification of an Annotation Protocol for Radiological Images - MRI

Author
Joana Rita Pereira Marques Bilreiro

Institution
UP-FCUP