Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

About

I'm Teresa Araújo and I'm a PhD student and researcher at INESC TEC and Faculdade de Engenharia da Universidade do Porto (FEUP).

My master degree is in Bioengineering (FEUP), specifically in the field of Biomedical Engineering. 

I am mainly interested in the fields of computer vision, machine learning and medical image analysis. My current research topic is the grading of diabetic retinopathy in color eye fundus images.

Interest
Topics
Details

Details

  • Name

    Teresa Finisterra Araújo
  • Role

    Research Assistant
  • Since

    01st September 2014
  • Contacts

    +351222094106
    teresa.f.araujo@inesctec.pt
001
Publications

2019

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery

Authors
Al Hajj, H; Lamard, M; Conze, PH; Roychowdhury, S; Hu, XW; Marsalkaite, G; Zisimopoulos, O; Dedmari, MA; Zhao, FQ; Prellberg, J; Sahu, M; Galdran, A; Araujo, T; Vo, DM; Panda, C; Dahiya, N; Kondo, S; Bian, ZB; Vandat, A; Bialopetravicius, J; Flouty, E; Qiu, CH; Dill, S; Mukhopadhyay, A; Costa, P; Aresta, G; Ramamurthys, S; Lee, SW; Campilho, A; Zachow, S; Xia, SR; Conjeti, S; Stoyanov, D; Armaitis, J; Heng, PA; Macready, WG; Cochener, B; Quellec, G;

Publication
Medical Image Analysis

Abstract
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future. © 2018 Elsevier B.V.

2019

Analysis of the performance of specialists and an automatic algorithm in retinal image quality assessment

Authors
Wanderley, DS; Araujo, T; Carvalho, CB; Maia, C; Penas, S; Carneiro, A; Mendonca, AM; Campilho, A;

Publication
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

Abstract

2019

EyeWeS: Weakly Supervised Pre-Trained Convolutional Neural Networks for Diabetic Retinopathy Detection

Authors
Costa, P; Araujo, T; Aresta, G; Galdran, A; Mendonca, AM; Smailagic, A; Campilho, A;

Publication
2019 16th International Conference on Machine Vision Applications (MVA)

Abstract

2019

BACH: Grand challenge on breast cancer histology images

Authors
Aresta, G; Araujo, T; Kwok, S; Chennamsetty, SS; Safwan, M; Alex, V; Marami, B; Prastawa, M; Chan, M; Donovan, M; Fernandez, G; Zeineh, J; Kohl, M; Walz, C; Ludwig, F; Braunewell, S; Baust, M; Vu, QD; To, MNN; Kim, E; Kwak, JT; Galal, S; Sanchez Freire, V; Brancati, N; Frucci, M; Riccio, D; Wang, YQ; Sun, LL; Ma, KQ; Fang, JN; Kone, ME; Boulmane, LS; Campilho, ARLO; Eloy, CTRN; Polonia, AONO; Aguiar, PL;

Publication
Medical Image Analysis

Abstract
Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. © 2019 Elsevier B.V.

2019

iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network

Authors
Aresta, G; Jacobs, C; Araujo, T; Cunha, A; Ramos, I; Ginneken, BV; Campilho, A;

Publication
CoRR

Abstract