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About

Aurélio Campilho is Professor in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Portugal. He is a Senior Member of the IEEE – The Institute of Electrical and Electronics Engineers. He is coordinator of the Center for Biomedical Engineering Research (C-BER) and develops research at the Biomedical Imaging Lab from C-BER from INESC TEC – Institute for Systems and Computer Engineering, Technology and Science. His teaching activities are in the  courses: Bioengineering Master Degree: Introduction to Scientific Computing, Biomedical Image Analysis and Computer-aided Diagnosis; Doctoral Degree in Electrical and Computer Engineering: Image Analysis and Recognition. His current research interests include the areas of biomedical engineering, medical image analysis, image processing and computer vision, particularly in Computer-aided Diagnosis applied in several imaging modalities, including ophthalmic images, carotid ultrasound imaging and computed tomography of the lung. He is General Chair of the series of International Conferences on Image Analysis and Recognition (ICIAR).

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Publications

2021

LNDb Challenge on automatic lung cancer patient management

Authors
Pedrosa, J; Aresta, G; Ferreira, C; Atwal, G; Phoulady, HA; Chen, XY; Chen, RZ; Li, JL; Wang, LS; Galdran, A; Bouchachia, H; Kaluva, KC; Vaidhya, K; Chunduru, A; Tarai, S; Nadimpalli, SPP; Vaidya, S; Kim, I; Rassadin, A; Tian, ZH; Sun, ZW; Jia, YZ; Men, XJ; Ramos, I; Cunha, A; Campilho, A;

Publication
Medical Image Analysis

Abstract

2021

Epistemic and Heteroscedastic Uncertainty Estimation in Retinal Blood Vessel Segmentation

Authors
Costa, P; Smailagic, A; Cardoso, JS; Campilho, A;

Publication
U.Porto Journal of Engineering

Abstract
Current state-of-the-art medical image segmentation methods require high quality datasets to obtain good performance. However, medical specialists often disagree on diagnosis, hence, datasets contain contradictory annotations. This, in turn, leads to difficulties in the optimization process of Deep Learning models and hinder performance. We propose a method to estimate uncertainty in Convolutional Neural Network (CNN) segmentation models, that makes the training of CNNs more robust to contradictory annotations. In this work, we model two types of uncertainty, heteroscedastic and epistemic, without adding any additional supervisory signal other than the ground-truth segmentation mask. As expected, the uncertainty is higher closer to vessel boundaries, and on top of thinner and less visible vessels where it is more likely for medical specialists to disagree. Therefore, our method is more suitable to learn from datasets created with heterogeneous annotators. We show that there is a correlation between the uncertainty estimated by our method and the disagreement in the segmentation provided by two different medical specialists. Furthermore, by explicitly modeling the uncertainty, the Intersection over Union of the segmentation network improves 5.7 percentage points.

2021

Automatic classification of retinal blood vessels based on multilevel thresholding and graph propagation

Authors
Remeseiro, B; Mendonça, AM; Campilho, A;

Publication
Vis. Comput.

Abstract

2021

A multi-task CNN approach for lung nodule malignancy classification and characterization

Authors
Marques, S; Schiavo, F; Ferreira, CA; Pedrosa, J; Cunha, A; Campilho, A;

Publication
Expert Systems with Applications

Abstract

2020

Automatic Lung Reference Model

Authors
Machado, M; Ferreira, CA; Pedrosa, J; Negrao, E; Rebelo, J; Leitao, P; Carvalho, AS; Rodrigues, MC; Ramos, I; Cunha, A; Campilho, A;

Publication
IFMBE Proceedings - XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019

Abstract

Supervised
thesis

2020

Lung nodule characterization and follow-up assessment

Author
Daniela Marisa da Silva Campos

Institution
UP-FEUP

2020

Diabetic Retinopathy Grading in Color Eye Fundus Images

Author
Teresa Manuel Sá Finisterra Araújo

Institution
UP-FEUP

2020

Detection of lung nodules in computed tomography images

Author
Guilherme Moreira Aresta

Institution
UP-FEUP

2020

Explainable Artificial Medical Intelligence for Automated Thoracic Pathology Screening

Author
Joana Maria Neves da Rocha

Institution
UP-FEUP

2020

Explainable AI-based Decision Support Models for COVID-19 Detection

Author
Sofia Perestrelo de Vasconcelos Cardoso Pereira

Institution
UP-FCUP