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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

002
Publications

2020

Weakly-Supervised Classification of HER2 Expression in Breast Cancer Haematoxylin and Eosin Stained Slides

Authors
Oliveira, SP; Pinto, JR; Goncalves, T; Canas Marques, R; Cardoso, MJ; Oliveira, HP; Cardoso, JS;

Publication
Applied Sciences

Abstract
Human epidermal growth factor receptor 2 (HER2) evaluation commonly requires immunohistochemistry (IHC) tests on breast cancer tissue, in addition to the standard haematoxylin and eosin (H&E) staining tests. Additional costs and time spent on further testing might be avoided if HER2 overexpression could be effectively inferred from H&E stained slides, as a preliminary indication of the IHC result. In this paper, we propose the first method that aims to achieve this goal. The proposed method is based on multiple instance learning (MIL), using a convolutional neural network (CNN) that separately processes H&E stained slide tiles and outputs an IHC label. This CNN is pretrained on IHC stained slide tiles but does not use these data during inference/testing. H&E tiles are extracted from invasive tumour areas segmented with the HASHI algorithm. The individual tile labels are then combined to obtain a single label for the whole slide. The network was trained on slides from the HER2 Scoring Contest dataset (HER2SC) and tested on two disjoint subsets of slides from the HER2SC database and the TCGA-TCIA-BRCA (BRCA) collection. The proposed method attained 83.3 % classification accuracy on the HER2SC test set and 53.8 % on the BRCA test set. Although further efforts should be devoted to achieving improved performance, the obtained results are promising, suggesting that it is possible to perform HER2 overexpression classification on H&E stained tissue slides.

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

2018

Three-dimensional planning tool for breast conserving surgery: A technological review

Authors
Oliveira, SP; Morgado, P; Gouveia, PF; Teixeira, JF; Bessa, S; Monteiro, JP; Zolfagharnasab, H; Reis, M; Silva, NL; Veiga, D; Cardoso, MJ; Oliveira, HP; Ferreira, MJ;

Publication
Critical Reviews in Biomedical Engineering

Abstract
Breast cancer is one of the most common malignanciesaffecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery. © 2018 by Begell House, Inc.

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