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Sobre
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Sobre

Sara Oliveira nasceu em Coimbra, Portugal, em 1992.

Obteve o grau de Mestre em Engenharia Biomédica pela Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Portugal, em 2016. Desde 2016 que é bolseira de investigação no INESC TEC, um instituto de I&D afiliado à Universidade do Porto, pertencendo ao grupo Visual Computing and Machine Intelligence Group (VCMI) e ao Breast Research Group. Atualmente, faz parte de um projeto de investigação financiado, BCCT.plan, relacionado com o planeamento do tratamento conservador do cancro da mama. Está também a frequentar o Programa Doutoral em Engenharia Eletrotécnica e de Computadores (PDEEC), na Faculdade de Engenharia da Universidade do Porto.

Os seus principais interesses de investigação incluem visão por computador, processamento de imagem, imagem médica, modelação 3D, machine learning e inteligência artificial.

Tópicos
de interesse
Detalhes

Detalhes

002
Publicações

2020

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

Autores
Oliveira, SP; Pinto, JR; Gonçalves, T; Canas Marques, R; Cardoso, MJ; Oliveira, HP; Cardoso, JS;

Publicação
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

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

Publicação
Sensors

Abstract

2018

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

Autores
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;

Publicação
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

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

Publicação
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.

Teses
supervisionadas

2017

Specification of an Annotation Protocol for Radiological Images - MRI

Autor
Joana Rita Pereira Marques Bilreiro

Instituição
UP-FCUP