P
Pedro Costa
Researcher at University of Porto
Publications - 14
Citations - 571
Pedro Costa is an academic researcher from University of Porto. The author has contributed to research in topics: Computer science & Feature (computer vision). The author has an hindex of 7, co-authored 9 publications receiving 450 citations.
Papers
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Journal ArticleDOI
End-to-End Adversarial Retinal Image Synthesis
Pedro Costa,Adrian Galdran,Maria Ines Meyer,Meindert Niemeijer,Michael D. Abràmoff,Ana Maria Mendonça,Aurélio Campilho +6 more
TL;DR: This paper proposes to implement an adversarial autoencoder for the task of retinal vessel network synthesis, and uses the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network.
Posted Content
Towards Adversarial Retinal Image Synthesis.
Pedro Costa,Adrian Galdran,Maria Ines Meyer,Michael D. Abràmoff,Meindert Niemeijer,Ana Maria Mendonça,Aurélio Campilho +6 more
TL;DR: This work proposes a method that learns to synthesize eye fundus images directly from data, by means of a vessel segmentation technique that uses a recent image-to-image translation technique, based on the idea of adversarial learning.
Posted Content
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis.
Adrian Galdran,Aitor Alvarez-Gila,Maria Ines Meyer,Cristina L. Saratxaga,Teresa Araújo,Estibaliz Garrote,Guilherme Aresta,Pedro Costa,Ana Maria Mendonça,Aurélio Campilho +9 more
TL;DR: This work applies the emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants, for training two deep convolutional neural networks for the tasks of skin lesion segmentation and skin lesions classification.
Journal ArticleDOI
Convolutional bag of words for diabetic retinopathy detection from eye fundus images
Pedro Costa,Aurélio Campilho +1 more
TL;DR: This paper describes a methodology for diabetic retinopathy detection from eye fundus images using a generalization of the bag-of-visual-words (BoVW) method as two neural networks that can be trained jointly.
Book ChapterDOI
A Deep Neural Network for Vessel Segmentation of Scanning Laser Ophthalmoscopy Images
TL;DR: This paper proposes a vessel segmentation technique for Scanning Laser Opthalmoscopy (SLO) retinal images that efficiently segments the vessel network, achieving a performance that outperforms the current state-of-the-art on this particular class of images.