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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 a Research at INESC TEC, an R&D institute affiliated with the University of Porto, in the Visual Computing and Machine Intelligence Group (VCMI) and in the Breast Research Group. Until August 2018, she was a project member in a funded research project, BCCT.plan, related with the planning of Breast Conserving Treatment. Currently, she is enrolled in the Doctoral Program in Electrical and Computer Engineering, at Faculty of Engineering of the University of Porto. Her research is focused on computational pathology approaches for breast, colorectal and cervical cancers diagnosis on whole slide images. Since July 2017, she is also a member of the organizing committee of VISUM (VIsion Understanding and Machine intelligence summer school). Her main research interests include computational pathology, medical image analysis, computer vision and machine (deep) learning.

Interest
Topics
Details

Details

002
Publications

2022

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

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

Publication
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

Authors
Albuquerque, T; Moreira, A; Barros, B; Montezuma, D; Oliveira, SP; Neto, PC; Monteiro, J; Ribeiro, L; Goncalves, S; Monteiro, A; Pinto, IM; Cardoso, JS;

Publication
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

Abstract

2021

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

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

Publication
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

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

Publication
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

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

Publication
SENSORS

Abstract

Supervised
thesis

2017

Media Content Personalisation Brokerage Platform

Author
Bruno Miguel Delindro Veloso

Institution
Outra