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

Overview of attention for book
Cover of 'Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support'

Table of Contents

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    Book Overview
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    Chapter 1 UNet++: A Nested U-Net Architecture for Medical Image Segmentation
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    Chapter 2 Deep Semi-supervised Segmentation with Weight-Averaged Consistency Targets
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    Chapter 3 Handling Missing Annotations for Semantic Segmentation with Deep ConvNets
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    Chapter 4 A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data
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    Chapter 5 Multi-scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification
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    Chapter 6 TreeNet: Multi-loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees
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    Chapter 7 3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation
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    Chapter 8 Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography
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    Chapter 9 MTMR-Net: Multi-task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis
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    Chapter 10 Active Deep Learning with Fisher Information for Patch-Wise Semantic Segmentation
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    Chapter 11 Contextual Additive Networks to Efficiently Boost 3D Image Segmentations
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    Chapter 12 Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration
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    Chapter 13 Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-organizing Maps
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    Chapter 14 Focal Dice Loss and Image Dilation for Brain Tumor Segmentation
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    Chapter 15 Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning
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    Chapter 16 3D Convolutional Neural Networks for Classification of Functional Connectomes
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    Chapter 17 Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks
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    Chapter 18 Unpaired Deep Cross-Modality Synthesis with Fast Training
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    Chapter 19 UOLO - Automatic Object Detection and Segmentation in Biomedical Images
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    Chapter 20 Unpaired Brain MR-to-CT Synthesis Using a Structure-Constrained CycleGAN
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    Chapter 21 Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
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    Chapter 22 Weakly Supervised Localisation for Fetal Ultrasound Images
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    Chapter 23 PIMMS: Permutation Invariant Multi-modal Segmentation
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    Chapter 24 Unsupervised Feature Learning for Outlier Detection with Stacked Convolutional Autoencoders, Siamese Networks and Wasserstein Autoencoders: Application to Epilepsy Detection
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    Chapter 25 Semi-automated Extraction of Crohns Disease MR Imaging Markers Using a 3D Residual CNN with Distance Prior
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    Chapter 26 Learning Optimal Deep Projection of $$^{18}$$ F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes
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    Chapter 27 Learning to Segment Medical Images with Scribble-Supervision Alone
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    Chapter 28 Learning to Decode 7T-Like MR Image Reconstruction from 3T MR Images
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    Chapter 29 Nonlinear Adaptively Learned Optimization for Object Localization in 3D Medical Images
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    Chapter 30 SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-Rays
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    Chapter 31 Monte-Carlo Sampling Applied to Multiple Instance Learning for Histological Image Classification
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    Chapter 32 Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
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    Chapter 33 Computed Tomography Image Enhancement Using 3D Convolutional Neural Network
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    Chapter 34 Paediatric Bone Age Assessment Using Deep Convolutional Neural Networks
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    Chapter 35 Automatic Myocardial Strain Imaging in Echocardiography Using Deep Learning
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    Chapter 36 Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-Slide Images
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    Chapter 37 Longitudinal Detection of Radiological Abnormalities with Time-Modulated LSTM
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    Chapter 38 Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease
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    Chapter 39 ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans
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    Chapter 40 A Multi-scale Multiple Sclerosis Lesion Change Detection in a Multi-sequence MRI
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    Chapter 41 Multi-task Sparse Low-Rank Learning for Multi-classification of Parkinson’s Disease
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    Chapter 42 Optic Disc Segmentation in Retinal Fundus Images Using Fully Convolutional Network and Removal of False-Positives Based on Shape Features
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    Chapter 43 Integrating Deformable Modeling with 3D Deep Neural Network Segmentation
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

37 tweeters
1 Facebook page


10 Dimensions

Readers on

64 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Published by
arXiv, December 2018
DOI 10.1007/978-3-030-00889-5
978-3-03-000888-8, 978-3-03-000889-5

Marc Combalia, Veronica Vilaplana, Combalia, Marc, Vilaplana Besler, Verónica


Stoyanov, Danail, Taylor, Zeike, Carneiro, Gustavo, Syeda-Mahmood, Tanveer, Martel, Anne, Maier-Hein, Lena, Tavares, João Manuel R.S., Bradley, Andrew, Papa, João Paulo, Belagiannis, Vasileios, Nascimento, Jacinto C., Lu, Zhi, Conjeti, Sailesh, Moradi, Mehdi, Greenspan, Hayit, Madabhushi, Anant

Twitter Demographics

The data shown below were collected from the profiles of 37 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 64 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Student > Master 12 19%
Researcher 5 8%
Student > Bachelor 5 8%
Student > Postgraduate 3 5%
Other 4 6%
Unknown 23 36%
Readers by discipline Count As %
Engineering 17 27%
Computer Science 17 27%
Medicine and Dentistry 3 5%
Physics and Astronomy 1 2%
Neuroscience 1 2%
Other 1 2%
Unknown 24 38%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 May 2020.
All research outputs
of 15,555,958 outputs
Outputs from arXiv
of 607,282 outputs
Outputs of similar age
of 278,122 outputs
Outputs of similar age from arXiv
of 24,776 outputs
Altmetric has tracked 15,555,958 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 607,282 research outputs from this source. They receive a mean Attention Score of 3.9. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 278,122 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 24,776 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.