Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
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

2023

Annotating for Artificial Intelligence Applications in Digital Pathology: A Practical Guide for Pathologists and Researchers

Autores
Montezuma, D; Oliveira, SP; Neto, PC; Oliveira, D; Monteiro, A; Cardoso, JS; Macedo-Pinto, I;

Publicação
MODERN PATHOLOGY

Abstract

2023

A CAD system for automatic dysplasia grading on H&E cervical whole-slide images

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

Publicação
Scientific Reports

Abstract
Cervical cancer is the fourth most common female cancer worldwide and the fourth leading cause of cancer-related death in women. Nonetheless, it is also among the most successfully preventable and treatable types of cancer, provided it is early identified and properly managed. As such, the detection of pre-cancerous lesions is crucial. These lesions are detected in the squamous epithelium of the uterine cervix and are graded as low- or high-grade intraepithelial squamous lesions, known as LSIL and HSIL, respectively. Due to their complex nature, this classification can become very subjective. Therefore, the development of machine learning models, particularly directly on whole-slide images (WSI), can assist pathologists in this task. In this work, we propose a weakly-supervised methodology for grading cervical dysplasia, using different levels of training supervision, in an effort to gather a bigger dataset without the need of having all samples fully annotated. The framework comprises an epithelium segmentation step followed by a dysplasia classifier (non-neoplastic, LSIL, HSIL), making the slide assessment completely automatic, without the need for manual identification of epithelial areas. The proposed classification approach achieved a balanced accuracy of 71.07% and sensitivity of 72.18%, at the slide-level testing on 600 independent samples, which are publicly available upon reasonable request. © 2023, The Author(s).

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.

Teses
supervisionadas

2017

Specification of an Annotation Protocol for Radiological Images - MRI

Autor
Joana Rita Pereira Marques Bilreiro

Instituição
UP-FCUP