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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).

Interest
Topics
Details

Details

  • Name

    Aurélio Campilho
  • Role

    Affiliated Researcher
  • Since

    01st January 2014
  • Nationality

    Portugal
  • Contacts

    +351222094106
    aurelio.campilho@inesctec.pt
006
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

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

2021

Segmentation of COVID-19 Lesions in CT Images

Authors
Rocha, J; Pereira, S; Campilho, A; Mendonça, AM;

Publication
2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Abstract

2021

Review on Deep Learning Methods for Chest X-Ray based Abnormality Detection and Thoracic Pathology Classification

Authors
Rocha J.; Mendonça A.M.; Campilho A.;

Publication
U.Porto Journal of Engineering

Abstract
Backed by more powerful computational resources and optimized training routines, deep learning models have proven unprecedented performance and several benefits to extract information from chest X-ray data. This is one of the most common imaging exams, whose increasing demand is reflected in the aggravated radiologists’ workload. Consequently, healthcare would benefit from computer-aided diagnosis systems to prioritize certain exams and further identify possible pathologies. Pioneering work in chest X-ray analysis has focused on the identification of specific diseases, but to the best of the authors' knowledge no paper has specifically reviewed relevant work on abnormality detection and multi-label thoracic pathology classification. This paper focuses on those issues, selecting the leading chest X-ray based deep learning strategies for comparison. In addition, the paper discloses the current annotated public chest X-ray databases, covering the common thorax diseases.

Supervised
thesis

2021

content based image retrieval as a computer aided diagnosis tool for radiologists

Author
José Ricardo Ferreira de Castro Ramos

Institution
UP-FEUP

2021

Lung nodule characterization and follow-up assessment

Author
Daniela Marisa da Silva Campos

Institution
UP-FEUP

2021

Detection of lung nodules in computed tomography images

Author
Guilherme Moreira Aresta

Institution
UP-FEUP

2021

Computer-aided diagnosis and follow-up of prevalent eye diseases using OCT/OCTA images

Author
Tânia Filipa Fernandes de Melo

Institution
UP-FEUP

2021

Explainable Artificial Medical Intelligence for Automated Thoracic Pathology Screening

Author
Joana Maria Neves da Rocha

Institution
UP-FEUP