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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

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Cover of 'Medical Image Computing and Computer Assisted Intervention – MICCAI 2020'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Attention, Suggestion and Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
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    Chapter 2 Scribble2Label: Scribble-Supervised Cell Segmentation via Self-generating Pseudo-Labels with Consistency
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    Chapter 3 Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides
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    Chapter 4 Deep Reinforcement Active Learning for Medical Image Classification
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    Chapter 5 An Effective Data Refinement Approach for Upper Gastrointestinal Anatomy Recognition
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    Chapter 6 Synthetic Sample Selection via Reinforcement Learning
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    Chapter 7 Dual-Level Selective Transfer Learning for Intrahepatic Cholangiocarcinoma Segmentation in Non-enhanced Abdominal CT
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    Chapter 8 BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture
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    Chapter 9 Constrain Latent Space for Schizophrenia Classification via Dual Space Mapping Net
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    Chapter 10 Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data
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    Chapter 11 Learning and Exploiting Interclass Visual Correlations for Medical Image Classification
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    Chapter 12 Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation
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    Chapter 13 Deep kNN for Medical Image Classification
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    Chapter 14 Learning Semantics-Enriched Representation via Self-discovery, Self-classification, and Self-restoration
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    Chapter 15 DECAPS: Detail-Oriented Capsule Networks
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    Chapter 16 Federated Simulation for Medical Imaging
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    Chapter 17 Continual Learning of New Diseases with Dual Distillation and Ensemble Strategy
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    Chapter 18 Learning to Segment When Experts Disagree
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    Chapter 19 Deep Disentangled Hashing with Momentum Triplets for Neuroimage Search
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    Chapter 20 Learning Joint Shape and Appearance Representations with Metamorphic Auto-Encoders
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    Chapter 21 Collaborative Learning of Cross-channel Clinical Attention for Radiotherapy-Related Esophageal Fistula Prediction from CT
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    Chapter 22 Learning Bronchiole-Sensitive Airway Segmentation CNNs by Feature Recalibration and Attention Distillation
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    Chapter 23 Learning Rich Attention for Pediatric Bone Age Assessment
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    Chapter 24 Weakly Supervised Organ Localization with Attention Maps Regularized by Local Area Reconstruction
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    Chapter 25 High-Order Attention Networks for Medical Image Segmentation
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    Chapter 26 NAS-SCAM: Neural Architecture Search-Based Spatial and Channel Joint Attention Module for Nuclei Semantic Segmentation and Classification
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    Chapter 27 Scientific Discovery by Generating Counterfactuals Using Image Translation
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    Chapter 28 Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction
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    Chapter 29 Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses
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    Chapter 30 Interpretability-Guided Content-Based Medical Image Retrieval
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    Chapter 31 Domain Aware Medical Image Classifier Interpretation by Counterfactual Impact Analysis
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    Chapter 32 Towards Emergent Language Symbolic Semantic Segmentation and Model Interpretability
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    Chapter 33 Meta Corrupted Pixels Mining for Medical Image Segmentation
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    Chapter 34 UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
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    Chapter 35 Difficulty-Aware Meta-learning for Rare Disease Diagnosis
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    Chapter 36 Few Is Enough: Task-Augmented Active Meta-learning for Brain Cell Classification
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    Chapter 37 Automatic Data Augmentation for 3D Medical Image Segmentation
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    Chapter 38 MS-NAS: Multi-scale Neural Architecture Search for Medical Image Segmentation
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    Chapter 39 Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs by Comparing Image Representations
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    Chapter 40 Dual-Task Self-supervision for Cross-modality Domain Adaptation
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    Chapter 41 Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-Efficient Cardiac Segmentation
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    Chapter 42 Test-Time Unsupervised Domain Adaptation
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    Chapter 43 Self Domain Adapted Network
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    Chapter 44 Entropy Guided Unsupervised Domain Adaptation for Cross-Center Hip Cartilage Segmentation from MRI
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    Chapter 45 User-Guided Domain Adaptation for Rapid Annotation from User Interactions: A Study on Pathological Liver Segmentation
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    Chapter 46 SALAD: Self-supervised Aggregation Learning for Anomaly Detection on X-Rays
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    Chapter 47 Scribble-Based Domain Adaptation via Co-segmentation
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    Chapter 48 Source-Relaxed Domain Adaptation for Image Segmentation
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    Chapter 49 Region-of-Interest Guided Supervoxel Inpainting for Self-supervision
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    Chapter 50 Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation
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    Chapter 51 Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
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    Chapter 52 DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images
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    Chapter 53 Double-Uncertainty Weighted Method for Semi-supervised Learning
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    Chapter 54 Shape-Aware Semi-supervised 3D Semantic Segmentation for Medical Images
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    Chapter 55 Local and Global Structure-Aware Entropy Regularized Mean Teacher Model for 3D Left Atrium Segmentation
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    Chapter 56 Improving Dense Pixelwise Prediction of Epithelial Density Using Unsupervised Data Augmentation for Consistency Regularization
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    Chapter 57 Knowledge-Guided Pretext Learning for Utero-Placental Interface Detection
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    Chapter 58 Self-supervised Depth Estimation to Regularise Semantic Segmentation in Knee Arthroscopy
  60. Altmetric Badge
    Chapter 59 Semi-supervised Medical Image Classification with Global Latent Mixing
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    Chapter 60 Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-supervised Medical Image Segmentation
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    Chapter 61 Semi-supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is Not Mean
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    Chapter 62 Predicting Potential Propensity of Adolescents to Drugs via New Semi-supervised Deep Ordinal Regression Model
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    Chapter 63 Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-supervised Learning and Dual-UNet
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    Chapter 64 Domain Adaptive Relational Reasoning for 3D Multi-organ Segmentation
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    Chapter 65 Realistic Adversarial Data Augmentation for MR Image Segmentation
  67. Altmetric Badge
    Chapter 66 Learning to Segment Anatomical Structures Accurately from One Exemplar
  68. Altmetric Badge
    Chapter 67 Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning
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    Chapter 68 Extreme Consistency: Overcoming Annotation Scarcity and Domain Shifts
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    Chapter 69 Spatio-Temporal Consistency and Negative Label Transfer for 3D Freehand US Segmentation
  71. Altmetric Badge
    Chapter 70 Characterizing Label Errors: Confident Learning for Noisy-Labeled Image Segmentation
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    Chapter 71 Leveraging Undiagnosed Data for Glaucoma Classification with Teacher-Student Learning
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    Chapter 72 Difficulty-Aware Glaucoma Classification with Multi-rater Consensus Modeling
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    Chapter 73 Intra-operative Forecasting of Growth Modulation Spine Surgery Outcomes with Spatio-Temporal Dynamic Networks
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    Chapter 74 Self-supervision on Unlabelled or Data for Multi-person 2D/3D Human Pose Estimation
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    Chapter 75 Knowledge Distillation from Multi-modal to Mono-modal Segmentation Networks
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    Chapter 76 Heterogeneity Measurement of Cardiac Tissues Leveraging Uncertainty Information from Image Segmentation
  78. Altmetric Badge
    Chapter 77 Efficient Shapley Explanation for Features Importance Estimation Under Uncertainty
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    Chapter 78 Cartilage Segmentation in High-Resolution 3D Micro-CT Images via Uncertainty-Guided Self-training with Very Sparse Annotation
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    Chapter 79 Probabilistic 3D Surface Reconstruction from Sparse MRI Information
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    Chapter 80 Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology?
  82. Altmetric Badge
    Chapter 81 Deep Generative Model for Synthetic-CT Generation with Uncertainty Predictions
Attention for Chapter 67: Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning
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Chapter title
Uncertainty Estimates as Data Selection Criteria to Boost Omni-Supervised Learning
Chapter number 67
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Published by
Springer, Cham, September 2020
DOI 10.1007/978-3-030-59710-8_67
Book ISBNs
978-3-03-059709-2, 978-3-03-059710-8
Authors

Lorenzo Venturini, Aris T. Papageorghiou, J. Alison Noble, Ana I. L. Namburete, Venturini, Lorenzo, Papageorghiou, Aris T., Noble, J. Alison, Namburete, Ana I. L.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Student > Master 3 12%
Researcher 2 8%
Student > Doctoral Student 1 4%
Other 1 4%
Other 1 4%
Unknown 12 48%
Readers by discipline Count As %
Computer Science 6 24%
Engineering 3 12%
Agricultural and Biological Sciences 2 8%
Mathematics 1 4%
Medicine and Dentistry 1 4%
Other 1 4%
Unknown 11 44%