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Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Overview of attention for book
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and…
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Noise as Domain Shift: Denoising Medical Images by Unpaired Image Translation
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    Chapter 2 Temporal Consistency Objectives Regularize the Learning of Disentangled Representations
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    Chapter 3 Multi-layer Domain Adaptation for Deep Convolutional Networks
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    Chapter 4 Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training
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    Chapter 5 Learning Interpretable Disentangled Representations Using Adversarial VAEs
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    Chapter 6 Synthesising Images and Labels Between MR Sequence Types with CycleGAN
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    Chapter 7 Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning
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    Chapter 8 Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans
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    Chapter 9 A Pulmonary Nodule Detection Method Based on Residual Learning and Dense Connection
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    Chapter 10 Harmonization and Targeted Feature Dropout for Generalized Segmentation: Application to Multi-site Traumatic Brain Injury Images
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    Chapter 11 Improving Pathological Structure Segmentation via Transfer Learning Across Diseases
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    Chapter 12 Generating Virtual Chromoendoscopic Images and Improving Detectability and Classification Performance of Endoscopic Lesions
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    Chapter 13 Self-supervised Learning of Inverse Problem Solvers in Medical Imaging
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    Chapter 14 Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-Propagation
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    Chapter 15 A Cascade Attention Network for Liver Lesion Classification in Weakly-Labeled Multi-phase CT Images
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    Chapter 16 CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT
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    Chapter 17 Active Learning Technique for Multimodal Brain Tumor Segmentation Using Limited Labeled Images
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    Chapter 18 Semi-supervised Learning of Fetal Anatomy from Ultrasound
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    Chapter 19 Multi-modal Segmentation with Missing MR Sequences Using Pre-trained Fusion Networks
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    Chapter 20 More Unlabelled Data or Label More Data? A Study on Semi-supervised Laparoscopic Image Segmentation
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    Chapter 21 Few-Shot Learning with Deep Triplet Networks for Brain Imaging Modality Recognition
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    Chapter 22 A Convolutional Neural Network Method for Boundary Optimization Enables Few-Shot Learning for Biomedical Image Segmentation
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    Chapter 23 Transfer Learning from Partial Annotations for Whole Brain Segmentation
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    Chapter 24 Learning to Segment Skin Lesions from Noisy Annotations
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    Chapter 25 A Weakly Supervised Method for Instance Segmentation of Biological Cells
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    Chapter 26 Towards Practical Unsupervised Anomaly Detection on Retinal Images
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    Chapter 27 Fine Tuning U-Net for Ultrasound Image Segmentation: Which Layers?
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    Chapter 28 Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance
Overall attention for this book and its chapters
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Mentioned by

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3 X users
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2 patents

Citations

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14 Dimensions

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Title
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data
Published by
Springer International Publishing, December 2019
DOI 10.1007/978-3-030-33391-1
ISBNs
978-3-03-033390-4, 978-3-03-033391-1
Editors

Wang, Qian, Milletari, Fausto, Nguyen, Hien V., Albarqouni, Shadi, Cardoso, M. Jorge, Rieke, Nicola, Xu, Ziyue, Kamnitsas, Konstantinos, Patel, Vishal, Roysam, Badri, Jiang, Steve, Zhou, Kevin, Luu, Khoa, Le, Ngan

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 6 21%
Student > Ph. D. Student 3 11%
Student > Postgraduate 2 7%
Professor > Associate Professor 1 4%
Student > Doctoral Student 1 4%
Other 0 0%
Unknown 15 54%
Readers by discipline Count As %
Computer Science 6 21%
Engineering 5 18%
Mathematics 1 4%
Unknown 16 57%

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