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Domain-adversarial neural networks to address the appearance variability of histopathology images

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  of histopathology images'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Simultaneous Multiple Surface Segmentation Using Deep Learning
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    Chapter 2 A Deep Residual Inception Network for HEp-2 Cell Classification
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    Chapter 3 Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures
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    Chapter 4 Accelerated Magnetic Resonance Imaging by Adversarial Neural Network
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    Chapter 5 Left Atrium Segmentation in CT Volumes with Fully Convolutional Networks
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    Chapter 6 3D Randomized Connection Network with Graph-Based Inference
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    Chapter 7 Adversarial Training and Dilated Convolutions for Brain MRI Segmentation
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    Chapter 8 CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography
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    Chapter 9 Region-Aware Deep Localization Framework for Cervical Vertebrae in X-Ray Images
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    Chapter 10 Domain-Adversarial Neural Networks to Address the Appearance Variability of Histopathology Images
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    Chapter 11 Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks
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    Chapter 12 Deep Residual Recurrent Neural Networks for Characterisation of Cardiac Cycle Phase from Echocardiograms
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    Chapter 13 Computationally Efficient Cardiac Views Projection Using 3D Convolutional Neural Networks
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    Chapter 14 Non-rigid Craniofacial 2D-3D Registration Using CNN-Based Regression
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    Chapter 15 A Deep Level Set Method for Image Segmentation
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    Chapter 16 Context-Based Normalization of Histological Stains Using Deep Convolutional Features
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    Chapter 17 Transitioning Between Convolutional and Fully Connected Layers in Neural Networks
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    Chapter 18 Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks
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    Chapter 19 Multi-stage Diagnosis of Alzheimer’s Disease with Incomplete Multimodal Data via Multi-task Deep Learning
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    Chapter 20 A Multi-scale CNN and Curriculum Learning Strategy for Mammogram Classification
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    Chapter 21 Analyzing Microscopic Images of Peripheral Blood Smear Using Deep Learning
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    Chapter 22 AGNet: Attention-Guided Network for Surgical Tool Presence Detection
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    Chapter 23 Pathological Pulmonary Lobe Segmentation from CT Images Using Progressive Holistically Nested Neural Networks and Random Walker
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    Chapter 24 End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
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    Chapter 25 Stain Colour Normalisation to Improve Mitosis Detection on Breast Histology Images
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    Chapter 26 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation
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    Chapter 27 A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
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    Chapter 28 Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations
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    Chapter 29 ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features
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    Chapter 30 Fully Convolutional Regression Network for Accurate Detection of Measurement Points
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    Chapter 31 Fast Predictive Simple Geodesic Regression
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    Chapter 32 Learning Spatio-Temporal Aggregation for Fetal Heart Analysis in Ultrasound Video
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    Chapter 33 Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks
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    Chapter 34 Self-supervised Learning for Spinal MRIs
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    Chapter 35 Skin Lesion Segmentation via Deep RefineNet
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    Chapter 36 Multi-scale Networks for Segmentation of Brain Magnetic Resonance Images
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    Chapter 37 Deep Learning for Automatic Detection of Abnormal Findings in Breast Mammography
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    Chapter 38 Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections
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    Chapter 39 Mapping Multi-Modal Routine Imaging Data to a Single Reference via Multiple Templates
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    Chapter 40 Automated Detection of Epileptogenic Cortical Malformations Using Multimodal MRI
  42. Altmetric Badge
    Chapter 41 Prediction of Amyloidosis from Neuropsychological and MRI Data for Cost Effective Inclusion of Pre-symptomatic Subjects in Clinical Trials
  43. Altmetric Badge
    Chapter 42 Automated Multimodal Breast CAD Based on Registration of MRI and Two View Mammography
  44. Altmetric Badge
    Chapter 43 EMR-Radiological Phenotypes in Diseases of the Optic Nerve and Their Association with Visual Function
  45. Altmetric Badge
    Chapter 44 Erratum to: Fast Predictive Simple Geodesic Regression
Attention for Chapter 3: Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures
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Chapter title
Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures
Chapter number 3
Book title
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
Published in
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, Quebec City, QC..., September 2017
DOI 10.1007/978-3-319-67558-9_3
Pubmed ID
Book ISBNs
978-3-31-967557-2, 978-3-31-967558-9

Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan, Trullo, Roger, Petitjean, Caroline, Nie, Dong, Shen, Dinggang, Ruan, Su


Computed Tomography (CT) is the standard imaging technique for radiotherapy planning. The delineation of Organs at Risk (OAR) in thoracic CT images is a necessary step before radiotherapy, for preventing irradiation of healthy organs. However, due to low contrast, multi-organ segmentation is a challenge. In this paper, we focus on developing a novel framework for automatic delineation of OARs. Different from previous works in OAR segmentation where each organ is segmented separately, we propose two collaborative deep architectures to jointly segment all organs, including esophagus, heart, aorta and trachea. Since most of the organ borders are ill-defined, we believe spatial relationships must be taken into account to overcome the lack of contrast. The aim of combining two networks is to learn anatomical constraints with the first network, which will be used in the second network, when each OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Conditional Random Fields (CRF). Next, the second deep architecture is employed to refine the segmentation of each organ by using the maps obtained on the first deep architecture to learn anatomical constraints for guiding and refining the segmentations. Experimental results show superior performance on 30 CT scans, comparing with other state-of-the-art methods.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 23%
Student > Master 7 20%
Student > Ph. D. Student 6 17%
Student > Doctoral Student 2 6%
Student > Bachelor 2 6%
Other 2 6%
Unknown 8 23%
Readers by discipline Count As %
Computer Science 12 34%
Engineering 6 17%
Medicine and Dentistry 3 9%
Physics and Astronomy 2 6%
Agricultural and Biological Sciences 1 3%
Other 3 9%
Unknown 8 23%