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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

006
Publications

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

2020

Automatic lung nodule detection combined with gaze information improves radiologists' screening performance

Authors
Aresta, G; Ferreira, C; Pedrosa, J; Araujo, T; Rebelo, J; Negrao, E; Morgado, M; Alves, F; Cunha, A; Ramos, I; Campilho, A;

Publication
IEEE Journal of Biomedical and Health Informatics

Abstract

2020

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Authors
Porwal, P; Pachade, S; Kokare, M; Deshmukh, G; Son, J; Bae, W; Liu, LH; Wang, J; Liu, XH; Gao, LX; Wu, TB; Xiao, J; Wang, FY; Yin, BC; Wang, YZ; Danala, G; He, LS; Choi, YH; Lee, YC; Jung, SH; Li, ZY; Sui, XD; Wu, JY; Li, XL; Zhou, T; Toth, J; Bara, A; Kori, A; Chennamsetty, SS; Safwan, M; Alex, V; Lyu, XZ; Cheng, L; Chu, QH; Li, PC; Ji, X; Zhang, SY; Shen, YX; Dai, L; Saha, O; Sathish, R; Melo, T; Araujo, T; Harangi, B; Sheng, B; Fang, RG; Sheet, D; Hajdu, A; Zheng, YJ; Mendonca, AM; Zhang, ST; Campilho, A; Zheng, B; Shen, D; Giancardo, L; Quellec, G; Meriaudeau, F;

Publication
Medical Image Analysis

Abstract

2020

LNDetector: A Flexible Gaze Characterisation Collaborative Platform for Pulmonary Nodule Screening

Authors
Pedrosa, J; Aresta, G; Rebelo, J; Negrao, E; Ramos, I; Cunha, A; Campilho, A;

Publication
IFMBE Proceedings

Abstract
Lung cancer is the deadliest type of cancer worldwide and late detection is one of the major factors for the low survival rate of patients. Low dose computed tomography has been suggested as a potential early screening tool but manual screening is costly, time-consuming and prone to interobserver variability. This has fueled the development of automatic methods for the detection, segmentation and characterisation of pulmonary nodules but its application to the clinical routine is challenging. In this study, a platform for the development, deployment and testing of pulmonary nodule computer-aided strategies is presented: LNDetector. LNDetector integrates image exploration and nodule annotation tools as well as advanced nodule detection, segmentation and classification methods and gaze characterisation. Different processing modules can easily be implemented or replaced to test their efficiency in clinical environments and the use of gaze analysis allows for the development of collaborative strategies. The potential use of this platform is shown through a combination of visual search, gaze characterisation and automatic nodule detection tools for an efficient and collaborative computer-aided strategy for pulmonary nodule screening. © 2020, Springer Nature Switzerland AG.

2020

DR|GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images

Authors
Araujo, T; Aresta, G; Mendonca, L; Penas, S; Maia, C; Carneiro, A; Maria Mendonca, AM; Campilho, A;

Publication
Medical Image Analysis

Abstract

Supervised
thesis

2020

Detection of lung nodules in computed tomography images

Author
Guilherme Moreira Aresta

Institution
UP-FEUP

2020

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

Author
José Ricardo Ferreira de Castro Ramos

Institution
UP-FEUP

2020

Explainable Artificial Medical Intelligence for Automated Thoracic Pathology Screening

Author
Joana Maria Neves da Rocha

Institution
UP-FEUP

2020

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

Author
Tânia Filipa Fernandes de Melo

Institution
UP-FEUP

2020

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

Author
Sofia Perestrelo de Vasconcelos Cardoso Pereira

Institution
UP-FCUP