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About

I began my academic life in 2007 by joining the Bachelor of Computing Engineering and Medical Instrumentation, at ISEP. After concluding my bachelor degree in 2010, I’ve joined the master degree in biomedical engineering, at FEUP. There I worked under the supervision of Professor Aurélio Campilho and I developed an ultrasound classification system used to the detection of the carotid walls. After the conclusion of my master degree, I moved to Belgium where I did my PhD. In my PhD I’ve developed a system which allows clinicians to quantify the tissue deformation patterns of the Achilles tendon in-vivo.

Since March 2017 I've been collaborating with INESCTEC. I am working on C-BER where we have been developing a computer-aided diagnosis system for the prevention and early diagnosis of pathologies of the female reproductive system.

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Details

Details

004
Publications

2020

A Multi-dataset Approach for DME Risk Detection in Eye Fundus Images

Authors
Carvalho, CB; Pedrosa, J; Maia, C; Penas, S; Carneiro, A; Mendonça, L; Mendonça, AM; Campilho, A;

Publication
Image Analysis and Recognition - 17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24-26, 2020, Proceedings, Part II

Abstract
Diabetic macular edema is a leading cause of visual loss for patients with diabetes. While diagnosis can only be performed by optical coherence tomography, diabetic macular edema risk assessment is often performed in eye fundus images in screening scenarios through the detection of hard exudates. Such screening scenarios are often associated with large amounts of data, high costs and high burden on specialists, motivating then the development of methodologies for automatic diabetic macular edema risk prediction. Nevertheless, significant dataset domain bias, due to different acquisition equipment, protocols and/or different populations can have significantly detrimental impact on the performance of automatic methods when transitioning to a new dataset, center or scenario. As such, in this study, a method based on residual neural networks is proposed for the classification of diabetic macular edema risk. This method is then validated across multiple public datasets, simulating the deployment in a multi-center setting and thereby studying the method’s generalization capability and existing dataset domain bias. Furthermore, the method is tested on a private dataset which more closely represents a realistic screening scenario. An average area under the curve across all public datasets of 0.891 ± 0.013 was obtained with a ResNet50 architecture trained on a limited amount of images from a single public dataset (IDRiD). It is also shown that screening scenarios are significantly more challenging and that training across multiple datasets leads to an improvement of performance (area under the curve of 0.911 ± 0.009). © Springer Nature Switzerland AG 2020.

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

End-to-End Ovarian Structures Segmentation

Authors
Wanderley, DS; Carvalho, CB; Domingues, A; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;

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

Abstract

2019

Deep Learning Approaches for Gynaecological Ultrasound Image Segmentation: A Radio-Frequency vs B-mode Comparison

Authors
Carvalho, C; Marques, S; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;

Publication
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2019

Segmentation of gynaecological ultrasound images using different U-Net based approaches

Authors
Marques, S; Carvalho, C; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;

Publication
2019 IEEE International Ultrasonics Symposium (IUS)

Abstract