Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Pedro M. M. Pereira"'
Autor:
Pedro M. M. Pereira, Lucas A. Thomaz, Luis M. N. Tavora, Pedro A. A. Assuncao, Rui Fonseca-Pinto, Rui Pedro Paiva, Sergio M. M. Faria
Publikováno v:
IEEE Access, Vol 10, Pp 76296-76309 (2022)
This work presents a contribution to advance current solutions for the problem of melanoma detection based on deep learning (DL) approaches. This is an active research field, which aims to aid on the detection and classification of melanoma (the most
Externí odkaz:
https://doaj.org/article/a1778f30c069424ea7f59a35f314b6a7
Autor:
Pedro M. M. Pereira, Lucas A. Thomaz, Luis M. N. Tavora, Pedro A. A. Assuncao, Rui Fonseca-Pinto, Rui Pedro Paiva, Sergio M. M. Faria
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
Machine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2
Autor:
Pedro Assuncao, Rui Pedro Paiva, Rui Fonseca-Pinto, Luis M. N. Tavora, Lucas A. Thomaz, Pedro M. M. Pereira, Sergio M. M. de Faria
Publikováno v:
Medical image analysis. 75
Medical image classification through learning-based approaches has been increasingly used, namely in the discrimination of melanoma. However, for skin lesion classification in general, such methods commonly rely on dermoscopic or other 2D-macro RGB i
Autor:
Pedro Assuncao, Rui Pedro Paiva, Luis M. N. Tavora, Rui Fonseca-Pinto, Sergio M. M. de Faria, Pedro M. M. Pereira, Lucas A. Thomaz
Publikováno v:
2021 Telecoms Conference (ConfTELE).
Computer-aided diagnostic has become a thriving research area in recent years, namely on the identification of skin lesions such as melanoma. This work presents a novel approach to this field by exploiting the 3D characteristics of the skin lesion su
Publikováno v:
Gazeta Médica.
INTRODUÇÃO: A gestão da multimorbilidade pode originar sobrecarga médica que deve ser avaliada.O objetivo do trabalho foi criar e validar um questionário para medir a sobrecarga pela gestão da multimorbilidade nos médicos de Medicina Geral e F
Autor:
Pedro M. M. Pereira, Luis M. N. Tavora, Jose N. Filipe, Pedro Assuncao, Rui Fonseca-Pinto, Victoria Guiote Dominguez, Martinha Henrique, Miguel O. Santos, Sergio M. M. de Faria, Felicidade Santiago
Publikováno v:
EMBC
Light field imaging technology has been attracting increasing interest because it enables capturing enriched visual information and expands the processing capabilities of traditional 2D imaging systems. Dense multiview, accurate depth maps and multip
Autor:
Pedro M. M. Pereira, Luis M. N. Tavora, Rui Fonseca-Pinto, Sergio M. M. de Faria, Pedro Assuncao, Rui Pedro Paiva
Publikováno v:
BIOIMAGING
Image segmentation is a key stage in medical image processing algorithms and machine learning classifiers where identification of discriminative features are of utmost importance. In the case of skin lesions, most of the existing image segmentation a
Autor:
Gabriel Falcao, Sergio M. M. de Faria, Nuno M. M. Rodrigues, Pedro M. M. Pereira, Patricio Domingues
Publikováno v:
International Journal of Distributed and Parallel systems. 7:01-20
This paper studies the performance and energy consumption of several multi-core, multi-CPUs and manycore hardware platforms and software stacks for parallel programming. It uses the Multimedia Multiscale Parser (MMP), a computationally demanding imag
Autor:
Pedro Assuncao, Rui Pedro Paiva, Pedro M. M. Pereira, Sergio M. M. de Faria, Luis M. N. Tavora, Lucas A. Thomaz, Rui Fonseca-Pinto
Publikováno v:
Biomedical Signal Processing and Control. 59:101924
Accurate skin lesion segmentation is important for identification and classification through computational methods. However, when performed by dermatologists, the results of clinical segmentation are affected by a certain margin of inaccuracy (which
Autor:
Pedro Assuncao, Rui Pedro Paiva, Rui Fonseca-Pinto, Luis M. N. Tavora, Lucas A. Thomaz, Pedro M. M. Pereira, Sergio M. M. de Faria
Publikováno v:
Biomedical Signal Processing and Control. 57:101765
Machine learning algorithms are progressively assuming an important role as a computational tool to support clinical diagnosis, namely in the classification of pigmented skin lesions. The current classification methods commonly rely on features deriv