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About

About

My name is Ana Maria Mendonça and I am currently Associate Professor at the Department of Electrical and Computer Engineering (DEEC) of the Faculty of Engineering of the University of Porto (FEUP), where I got my PhD in 1994. I was a researcher at the Institute for Biomedical Engineering (INEB) until 2014, but since 2015 I am a senior researcher at INESC TEC.

For several years, I was a member of the Executive council of DEEC and at present I am the Vice-Dean of FEUP. At INEB, I was a member of the Board of Directors and afterwards President of the Board. I was an elected member of the pedagogical council of FEUP and currently I am also a member of the scientific council. I was the Director of the Master in Biomedical Engineering from 2009 to 2014 and I am a member of the scientific committee of the Doctoral Programme in Biomedical Engineering.

I have been collaborating as a research and also as responsible in several research projects, mostly dedicated to the development of image analysis and classification methodologies aiming at extracting essential information from medical images in order to support the diagnosis process. Past work has been mostly devoted to three main areas: retinal pathologies, lung diseases and genetic disorders, but ongoing work is mainly focused on the development of Computer-Aided Diagnosis systems in Ophthalmology and Radiology.

Interest
Topics
Details

Details

003
Publications

2019

An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans

Authors
Shakibapour, E; Cunha, A; Aresta, G; Mendonca, AM; Campilho, A;

Publication
Expert Systems with Applications

Abstract
This paper proposes a new methodology to automatically segment and measure the volume of pulmonary nodules in lung computed tomography (CT) scans. Estimating the malignancy likelihood of a pulmonary nodule based on lesion characteristics motivated the development of an unsupervised pulmonary nodule segmentation and volume measurement as a preliminary stage for pulmonary nodule characterization. The idea is to optimally cluster a set of feature vectors composed by intensity and shape-related features in a given feature data space extracted from a pre-detected nodule. For that purpose, a metaheuristic search based on evolutionary computation is used for clustering the corresponding feature vectors. The proposed method is simple, unsupervised and is able to segment different types of nodules in terms of location and texture without the need for any manual annotation. We validate the proposed segmentation and volume measurement on the Lung Image Database Consortium and Image Database Resource Initiative – LIDC-IDRI dataset. The first dataset is a group of 705 solid and sub-solid (assessed as part-solid and non-solid) nodules located in different regions of the lungs, and the second, more challenging, is a group of 59 sub-solid nodules. The average Dice scores of 82.35% and 71.05% for the two datasets show the good performance of the segmentation proposal. Comparisons with previous state-of-the-art techniques also show acceptable and comparable segmentation results. The volumes of the segmented nodules are measured via ellipsoid approximation. The correlation and statistical significance between the measured volumes of the segmented nodules and the ground-truth are obtained by Pearson correlation coefficient value, obtaining an R-value = 92.16% with a significance level of 5%. © 2018 Elsevier Ltd

2019

Convolutional Neural Network Architectures for Texture Classification of Pulmonary Nodules

Authors
Ferreira, CA; Cunha, A; Mendonça, AM; Campilho, A;

Publication
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

Abstract

2019

Wide Residual Network for Lung-Rads™ Screening Referral

Authors
Ferreira, CA; Aresta, G; Cunha, A; Mendonça, AM; Campilho, A;

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

Abstract

2019

Quantitative Assessment of Central Serous Chorioretinopathy in Angiographic Sequences of Retinal Images

Authors
Ferreira, CA; Penas, S; Silva, J; Mendonça, AM;

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

Supervised
thesis

2017

Quantitative assessment of Central Serous Chorioretinopathy in Angiographic sequences of retinal images

Author
Carlos Alexandre Nunes Ferreira

Institution
UP-FEUP

2016

Estimation of Vessel Caliber in Retinal Images

Author
Teresa Manuel Sá Finisterra Araújo

Institution
UP-FEUP

2016

Detection of red lesions associated with Diabetic Retinopathy

Author
Tânia Filipa Fernandes Melo

Institution
UP-FEUP

2016

Estimation of choroidal thickness in OCT images

Author
Simão Pedro Marques Pinto de Faria

Institution
UP-FEUP

2015

Advanced Image Analysis for the Assessment of Retinal Vascular Changes

Author
Behdad Dasht Bozorg

Institution
UP-FEUP