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Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning

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Cover of 'Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning'

Table of Contents

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    Book Overview
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    Chapter 1 $$\alpha $$ -UNet++: A Data-Driven Neural Network Architecture for Medical Image Segmentation
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    Chapter 2 DAPR-Net: Domain Adaptive Predicting-Refinement Network for Retinal Vessel Segmentation
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    Chapter 3 Augmented Radiology: Patient-Wise Feature Transfer Model for Glioma Grading
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    Chapter 4 Attention-Guided Deep Domain Adaptation for Brain Dementia Identification with Multi-site Neuroimaging Data
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    Chapter 5 Registration of Histopathology Images Using Self Supervised Fine Grained Feature Maps
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    Chapter 6 Cross-Modality Segmentation by Self-supervised Semantic Alignment in Disentangled Content Space
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    Chapter 7 Semi-supervised Pathology Segmentation with Disentangled Representations
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    Chapter 8 Domain Generalizer: A Few-Shot Meta Learning Framework for Domain Generalization in Medical Imaging
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    Chapter 9 Parts2Whole: Self-supervised Contrastive Learning via Reconstruction
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    Chapter 10 Cross-View Label Transfer in Knee MR Segmentation Using Iterative Context Learning
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    Chapter 11 Continual Class Incremental Learning for CT Thoracic Segmentation
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    Chapter 12 First U-Net Layers Contain More Domain Specific Information Than the Last Ones
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    Chapter 13 Siloed Federated Learning for Multi-centric Histopathology Datasets
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    Chapter 14 On the Fairness of Privacy-Preserving Representations in Medical Applications
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    Chapter 15 Inverse Distance Aggregation for Federated Learning with Non-IID Data
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    Chapter 16 Weight Erosion: An Update Aggregation Scheme for Personalized Collaborative Machine Learning
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    Chapter 17 Federated Gradient Averaging for Multi-Site Training with Momentum-Based Optimizers
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    Chapter 18 Federated Learning for Breast Density Classification: A Real-World Implementation
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    Chapter 19 Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning
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    Chapter 20 Fed-BioMed: A General Open-Source Frontend Framework for Federated Learning in Healthcare
Attention for Chapter 13: Siloed Federated Learning for Multi-centric Histopathology Datasets
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Citations

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Summary Dimensions citations
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Chapter title
Siloed Federated Learning for Multi-centric Histopathology Datasets
Chapter number 13
Book title
Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning
Published by
Springer, Cham, October 2020
DOI 10.1007/978-3-030-60548-3_13
Book ISBNs
978-3-03-060547-6, 978-3-03-060548-3
Authors

Mathieu Andreux, Jean Ogier du Terrail, Constance Beguier, Eric W. Tramel, Andreux, Mathieu, du Terrail, Jean Ogier, Beguier, Constance, Tramel, Eric W.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 21%
Researcher 11 18%
Student > Ph. D. Student 11 18%
Student > Doctoral Student 3 5%
Other 2 3%
Other 4 7%
Unknown 17 28%
Readers by discipline Count As %
Computer Science 26 43%
Engineering 7 11%
Mathematics 2 3%
Environmental Science 2 3%
Medicine and Dentistry 2 3%
Other 4 7%
Unknown 18 30%

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