Prof. Pedro Furtado Profile

Prof. Pedro Furtado

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Publications (5)

Proceedings Article | 30 June 2021 Paper

Experiments with loss function for improvement of multi-class segmentation of diabetic retinopathy lesions

Pedro Furtado

Proceedings Volume 11878, 118780M (2021) https://doi.org/10.1117/12.2599508

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Proceedings Article | 15 February 2021 Paper

Deep segmentation of abdominal organs from MRI: off-the- shelf architectures and improvements

Pedro Furtado

Proceedings Volume 11596, 115963J (2021) https://doi.org/10.1117/12.2580849

KEYWORDS: Image segmentation, Magnetic resonance imaging, Liver, Spleen, Visualization, Network architectures, Medical imaging, Kidney, Convolution

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Proceedings Article | 3 March 2020 Presentation + Paper

Deep semantic segmentation of Diabetic Retinopathy lesions: what metrics really tell us

Pedro Furtado

Proceedings Volume 11317, 113170O (2020) https://doi.org/10.1117/12.2549221

KEYWORDS: Image segmentation, Eye, Optical discs, Network architectures, Convolution, Computer programming, Visualization, Neural networks

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Proceedings Article | 15 March 2019 Presentation + Paper

Objects characterization-based approach to enhance detection of degree of malignancy in breast cancer histopathology

Pedro Furtado

Proceedings Volume 10949, 109491R (2019) https://doi.org/10.1117/12.2511986

KEYWORDS: Image classification, Classification systems, Cancer, Tumors, Image segmentation, Breast cancer, Neural networks, Tissues, Convolution, Feature extraction, Neurons, Medical imaging

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Proceedings Article | 19 June 2017 Paper

Experiments on automatic classification of tissue malignancy in the field of digital pathology

J. Pereira, R. Barata, Pedro Furtado

Proceedings Volume 10443, 1044312 (2017) https://doi.org/10.1117/12.2280294

KEYWORDS: Feature extraction, Tumor growth modeling, Image analysis, RGB color model, Feature selection, Image segmentation, Analytical research, Breast cancer, Image classification, Medical imaging, Tissues, Pathology, Digital imaging, Machine learning, Pattern recognition

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