Joao Paulo Papa – författare & böcker

Bio-Inspired Computation and Applications in Image Processing

E-bok

Engelska, 2016

1 681 kr

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Bio-Inspired Computation and Applications in Image Processing summarizes the latest developments in bio-inspired computation in image processing, focusing on nature-inspired algorithms that are linked with deep learning, such as ant colony optimization, particle swarm optimization, and bat and firefly algorithms that have recently emerged in the field.In addition to documenting state-of-the-art developments, this book also discusses future research trends in bio-inspired computation, helping researchers establish new research avenues to pursue.- Reviews the latest developments in bio-inspired computation in image processing- Focuses on the introduction and analysis of the key bio-inspired methods and techniques- Combines theory with real-world applications in image processing- Helps solve complex problems in image and signal processing- Contains a diverse range of self-contained case studies in real-world applications

Optimum-Path Forest

Theory, Algorithms, and Applications

AvJoao Paulo Papa,Alexandre Xavier Falcao

E-bok

Engelska, 2022

2 105 kr

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The Optimum-Path Forest (OPF) classifier was first published in 2008 in its supervised and unsupervised versions with applications in medicine and image classification. Since then, it has expanded to a variety of other applications such as remote sensing, electrical and petroleum engineering, and biology. In recent years, multi-label and semi-supervised versions were also developed to handle video classification problems. The book presents the principles, algorithms and applications of Optimum-Path Forest, giving the theory and state-of-the-art as well as insights into future directions.- Presents the first book on Optimum-path Forest- Shows how it can be used with Deep Learning- Gives a wide range of applications- Includes the methods, underlying theory and applications of Optimum-Path Forest (OPF)

Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, Lena Maier-Hein, João Manuel R.S. Tavares, Andrew Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi - Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Häftad

Del 11045 - Lecture Notes in Computer Science

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

AvDanail Stoyanov,Zeike Taylorm. fl.

Häftad, Engelska, 2018

565 kr

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This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Anant Madabhushi, Hayit Greenspan, Mehdi Moradi, Sailesh Conjeti, Zhi Lu, Jacinto C. Nascimento, Vasileios Belagiannis, Joao Paulo Papa, Andrew Bradley, Joao Manuel R.S. Tavares, Lena Maier-Hein, Anne Martel, Tanveer Syeda-Mahmood, Gustavo Carneiro, Zeike Taylor, Danail Stoyanov - Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, E-bok

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

AvAnant Madabhushi,Hayit Greenspanm. fl.

E-bok

Engelska, 2018

668 kr

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This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.

The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

João Manuel R. S. Tavares, João Paulo Papa, Manuel González Hidalgo - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Häftad

Del 12702 - Lecture Notes in Computer Science

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers

AvJoão Manuel R. S. Tavares,João Paulo Papam. fl.

Häftad, Engelska, 2022

899 kr

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This book constitutes the proceedings of the 25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021, which took place during May 10–13, 2021. The conference was initially planned to take place in Porto, Portugal, but changed to a virtual event due to the COVID-19 pandemic. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections as follows: medical applications; natural language processing; metaheuristics; image segmentation; databases; deep learning; explainable artificial intelligence; image processing; machine learning; and computer vision.

Manuel Gonzalez Hidalgo, Joao Paulo Papa, Joao Manuel R. S. Tavares - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, E-bok

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers

AvManuel Gonzalez Hidalgo,Joao Paulo Papam. fl.

E-bok

Engelska, 2022

1 101 kr

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This book constitutes the proceedings of the 25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021, which took place during May 10–13, 2021. The conference was initially planned to take place in Porto, Portugal, but changed to a virtual event due to the COVID-19 pandemic. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections as follows: medical applications; natural language processing; metaheuristics; image segmentation; databases; deep learning; explainable artificial intelligence; image processing; machine learning; and computer vision.

Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise - Deep Learning and Data Labeling for Medical Applications, Häftad

Del 10008 - Lecture Notes in Computer Science

Deep Learning and Data Labeling for Medical Applications

First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings

AvGustavo Carneiro,Diana Mateusm. fl.

Häftad, Engelska, 2016

565 kr

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This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

Julien Cornebise, Jaime S. Cardoso, Zhi Lu, Marco Loog, Jacinto C. Nascimento, Joao Paulo Papa, Vasileios Belagiannis, Joao Manuel R. S. Tavares, Andrew Bradley, Loic Peter, Diana Mateus, Gustavo Carneiro - Deep Learning and Data Labeling for Medical Applications, E-bok

Deep Learning and Data Labeling for Medical Applications

First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings

AvJulien Cornebise,Jaime S. Cardosom. fl.

E-bok

PDF, Engelska, 2016

687 kr

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This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as image description; medical imaging-based diagnosis; medical signal-based diagnosis; medical image reconstruction and model selection using deep learning techniques; meta-heuristic techniques for fine-tuning parameter in deep learning-based architectures; and applications based on deep learning techniques.

M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, João Manuel R.S. Tavares, Mehdi Moradi, Andrew Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu - Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Häftad

Del 10553 - Lecture Notes in Computer Science

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings

AvM. Jorge Cardoso,Tal Arbelm. fl.

Häftad, Engelska, 2017

711 kr

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This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017.The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Zhi Lu, Vasileios Belagiannis, Jaime S. Cardoso, Jacinto C. Nascimento, Anant Madabhushi, Joao Paulo Papa, Hayit Greenspan, Andrew Bradley, Mehdi Moradi, Joao Manuel R.S. Tavares, Tanveer Syeda-Mahmood, Gustavo Carneiro, Tal Arbel, M. Jorge Cardoso - Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, E-bok

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings

AvZhi Lu,Vasileios Belagiannism. fl.

E-bok

Engelska, 2017

847 kr

Läs direkt efter köp

This book constitutes the refereed joint proceedings of the Third International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017, and the 6th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017, held in conjunction with the 20th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2017, in Québec City, QC, Canada, in September 2017.

The 38 full papers presented at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were carefully reviewed and selected. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

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Joao Paulo Papa – författare

Bio-Inspired Computation and Applications in Image Processing

Optimum-Path Forest

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Deep Learning and Data Labeling for Medical Applications

Deep Learning and Data Labeling for Medical Applications

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

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