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Sobre

Sobre

Sara Pires Oliveira nasceu em Coimbra, Portugal, em 1992. Recebeu o grau de Mestre em Engenharia Biomédica pela Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Portugal, em 2016. Desde 2016 que trabalha como Assistente de Investigação no Centro de Telecomunicações e Multimédia, no grupo de investigação VCMI (Visual Computing and Machine Intelligence) e no BRG (Breast Research Group). Até Agosto de 2018, foi membro do projeto BCCT.plan, relacionado com o planeamento do tratamento conservador da mama. Atualmente, e desde setembro de 2018, está inscrita no Programa de Doutoramento em Engenharia Electrotécnica e Informática (PDEEC), na Faculdade de Engenharia da Universidade do Porto. A sua investigação centra-se em abordagens de patologia computacional para o diagnóstico de cancro da mama, cancro colorectal e cancro cervical em imagens digitalizadas de amostras histológicas. Desde Julho de 2017, é também membro da equipa organizadora da escola de verão VISUM (VIsion Understanding and Machine intelligence summer school). Os seus principais interesses de investigação incluem patologia computacional, análise de imagem médica, visão por computador e machine (deep) learning.

Tópicos
de interesse
Detalhes

Detalhes

002
Publicações

2022

iMIL4PATH: A Semi-Supervised Interpretable Approach for Colorectal Whole-Slide Images

Autores
Neto, PC; Oliveira, SP; Montezuma, D; Fraga, J; Monteiro, A; Ribeiro, L; Goncalves, S; Pinto, IM; Cardoso, JS;

Publicação
CANCERS

Abstract
Colorectal cancer (CRC) diagnosis is based on samples obtained from biopsies, assessed in pathology laboratories. Due to population growth and ageing, as well as better screening programs, the CRC incidence rate has been increasing, leading to a higher workload for pathologists. In this sense, the application of AI for automatic CRC diagnosis, particularly on whole-slide images (WSI), is of utmost relevance, in order to assist professionals in case triage and case review. In this work, we propose an interpretable semi-supervised approach to detect lesions in colorectal biopsies with high sensitivity, based on multiple-instance learning and feature aggregation methods. The model was developed on an extended version of the recent, publicly available CRC dataset (the CRC+ dataset with 4433 WSI), using 3424 slides for training and 1009 slides for evaluation. The proposed method attained 90.19% classification ACC, 98.8% sensitivity, 85.7% specificity, and a quadratic weighted kappa of 0.888 at slide-based evaluation. Its generalisation capabilities are also studied on two publicly available external datasets.

2022

Quality Control in Digital Pathology: Automatic Fragment Detection and Counting

Autores
Albuquerque, T; Moreira, A; Barros, B; Montezuma, D; Oliveira, SP; Neto, PC; Monteiro, JC; Ribeiro, L; Gonçalves, S; Monteiro, A; Pinto, IM; Cardoso, JS;

Publicação
44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC 2022, Glasgow, Scotland, United Kingdom, July 11-15, 2022

Abstract

2021

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Autores
Oliveira, SP; Neto, PC; Fraga, J; Montezuma, D; Monteiro, A; Monteiro, J; Ribeiro, L; Goncalves, S; Pinto, IM; Cardoso, JS;

Publicação
SCIENTIFIC REPORTS

Abstract
AbstractMost oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

2020

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

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

Publicação
APPLIED SCIENCES-BASEL

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

Teses
supervisionadas

2018

Design of a software system for processes improvement in a technology start-up

Autor
Joana Raquel da Silva Rodrigues

Instituição
UP-FEUP