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Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Overview of attention for book
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Multimodal Fusion of Brain Networks with Longitudinal Couplings
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    Chapter 2 Penalized Geodesic Tractography for Mitigating Gyral Bias
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    Chapter 3 Anchor-Constrained Plausibility (ACP): A Novel Concept for Assessing Tractography and Reducing False-Positives
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    Chapter 4 Tract-Specific Group Analysis in Fetal Cohorts Using in utero Diffusion Tensor Imaging
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    Chapter 5 Tract Orientation Mapping for Bundle-Specific Tractography
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    Chapter 6 A Multi-Tissue Global Estimation Framework for Asymmetric Fiber Orientation Distributions
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    Chapter 7 Better Fiber ODFs from Suboptimal Data with Autoencoder Based Regularization
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    Chapter 8 Identification of Gadolinium Contrast Enhanced Regions in MS Lesions Using Brain Tissue Microstructure Information Obtained from Diffusion and T2 Relaxometry MRI
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    Chapter 9 A Bayes Hilbert Space for Compartment Model Computing in Diffusion MRI
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    Chapter 10 Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning
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    Chapter 11 Identification of Species-Preserved Cortical Landmarks
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    Chapter 12 Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases
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    Chapter 13 Enhancing Clinical MRI Perfusion Maps with Data-Driven Maps of Complementary Nature for Lesion Outcome Prediction
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    Chapter 14 Harmonizing Diffusion MRI Data Across Magnetic Field Strengths
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    Chapter 15 Normative Modeling of Neuroimaging Data Using Scalable Multi-task Gaussian Processes
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    Chapter 16 Multi-layer Large-Scale Functional Connectome Reveals Infant Brain Developmental Patterns
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    Chapter 17 A Riemannian Framework for Longitudinal Analysis of Resting-State Functional Connectivity
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    Chapter 18 Elastic Registration of Single Subject Task Based fMRI Signals
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    Chapter 19 A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data
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    Chapter 20 3D Deep Convolutional Neural Network Revealed the Value of Brain Network Overlap in Differentiating Autism Spectrum Disorder from Healthy Controls
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    Chapter 21 Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)
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    Chapter 22 The Dynamic Measurements of Regional Brain Activity for Resting-State fMRI: d-ALFF, d-fALFF and d-ReHo
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    Chapter 23 rfDemons: Resting fMRI-Based Cortical Surface Registration Using the BrainSync Transform
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    Chapter 24 Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI
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    Chapter 25 Neural Activation Estimation in Brain Networks During Task and Rest Using BOLD-fMRI
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    Chapter 26 Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization
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    Chapter 27 Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI
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    Chapter 28 Identifying Personalized Autism Related Impairments Using Resting Functional MRI and ADOS Reports
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    Chapter 29 Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis
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    Chapter 30 Structured Deep Generative Model of fMRI Signals for Mental Disorder Diagnosis
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    Chapter 31 Cardiac Cycle Estimation for BOLD-fMRI
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    Chapter 32 Probabilistic Source Separation on Resting-State fMRI and Its Use for Early MCI Identification
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    Chapter 33 Identifying Brain Networks of Multiple Time Scales via Deep Recurrent Neural Network
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    Chapter 34 A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis
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    Chapter 35 A Region-of-Interest-Reweight 3D Convolutional Neural Network for the Analytics of Brain Information Processing
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    Chapter 36 Quantitative Deconvolution of fMRI Data with Multi-echo Sparse Paradigm Free Mapping
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    Chapter 37 Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks
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    Chapter 38 Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets
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    Chapter 39 Fast Mapping of the Eloquent Cortex by Learning L2 Penalties
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    Chapter 40 Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models
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    Chapter 41 Exploring Fiber Skeletons via Joint Representation of Functional Networks and Structural Connectivity
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    Chapter 42 Phase Angle Spatial Embedding (PhASE)
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    Chapter 43 Edema-Informed Anatomically Constrained Particle Filter Tractography
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    Chapter 44 Thalamic Nuclei Segmentation Using Tractography, Population-Specific Priors and Local Fibre Orientation
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    Chapter 45 On Quantifying Local Geometric Structures of Fiber Tracts
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    Chapter 46 Modeling Longitudinal Voxelwise Feature Change in Normal Aging with Spatial-Anatomical Regularization
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    Chapter 47 Volume-Based Analysis of 6-Month-Old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis
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    Chapter 48 A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis
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    Chapter 49 Graph of Brain Structures Grading for Early Detection of Alzheimer’s Disease
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    Chapter 50 Joint Prediction and Classification of Brain Image Evolution Trajectories from Baseline Brain Image with Application to Early Dementia
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    Chapter 51 Temporal Correlation Structure Learning for MCI Conversion Prediction
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    Chapter 52 Synthesizing Missing PET from MRI with Cycle-consistent Generative Adversarial Networks for Alzheimer’s Disease Diagnosis
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    Chapter 53 Exploratory Population Analysis with Unbalanced Optimal Transport
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    Chapter 54 Multi-modal Synthesis of ASL-MRI Features with KPLS Regression on Heterogeneous Data
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    Chapter 55 A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models
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    Chapter 56 Deep Convolutional Networks for Automated Detection of Epileptogenic Brain Malformations
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    Chapter 57 Binary Glioma Grading: Radiomics versus Pre-trained CNN Features
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    Chapter 58 Automatic Irregular Texture Detection in Brain MRI Without Human Supervision
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    Chapter 59 Learning Myelin Content in Multiple Sclerosis from Multimodal MRI Through Adversarial Training
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    Chapter 60 Deep Multi-structural Shape Analysis: Application to Neuroanatomy
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    Chapter 61 Computational Modelling of Pathogenic Protein Behaviour-Governing Mechanisms in the Brain
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    Chapter 62 Generative Discriminative Models for Multivariate Inference and Statistical Mapping in Medical Imaging
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    Chapter 63 Using the Anisotropic Laplace Equation to Compute Cortical Thickness
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    Chapter 64 Dilatation of Lateral Ventricles with Brain Volumes in Infants with 3D Transfontanelle US
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    Chapter 65 Do Baby Brain Cortices that Look Alike at Birth Grow Alike During the First Year of Postnatal Development?
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    Chapter 66 Multi-label Transduction for Identifying Disease Comorbidity Patterns
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    Chapter 67 Text to Brain: Predicting the Spatial Distribution of Neuroimaging Observations from Text Reports
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    Chapter 68 Semi-supervised Learning for Segmentation Under Semantic Constraint
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    Chapter 69 Autofocus Layer for Semantic Segmentation
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    Chapter 70 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
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    Chapter 71 Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding
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    Chapter 72 CompNet: Complementary Segmentation Network for Brain MRI Extraction
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    Chapter 73 One-Pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation
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    Chapter 74 Deep Recurrent Level Set for Segmenting Brain Tumors
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    Chapter 75 Pulse Sequence Resilient Fast Brain Segmentation
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    Chapter 76 Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks
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    Chapter 77 Registration-Free Infant Cortical Surface Parcellation Using Deep Convolutional Neural Networks
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    Chapter 78 Joint Segmentation of Intracerebral Hemorrhage and Infarct from Non-Contrast CT Images of Post-treatment Acute Ischemic Stroke Patients
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    Chapter 79 Patch-Based Mapping of Transentorhinal Cortex with a Distributed Atlas
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    Chapter 80 Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
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    Chapter 81 Adaptive Feature Recombination and Recalibration for Semantic Segmentation: Application to Brain Tumor Segmentation in MRI
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    Chapter 82 Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
Attention for Chapter 70: 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
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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 (86th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

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Chapter title
3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes
Chapter number 70
Book title
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Published in
arXiv, September 2018
DOI 10.1007/978-3-030-00931-1_70
Book ISBNs
978-3-03-000930-4, 978-3-03-000931-1
Authors

Ken C. L. Wong, Mehdi Moradi, Hui Tang, Tanveer Syeda-Mahmood

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X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 212 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 212 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 25%
Researcher 29 14%
Student > Master 29 14%
Student > Doctoral Student 11 5%
Student > Bachelor 7 3%
Other 18 8%
Unknown 66 31%
Readers by discipline Count As %
Computer Science 75 35%
Engineering 37 17%
Medicine and Dentistry 5 2%
Neuroscience 4 2%
Biochemistry, Genetics and Molecular Biology 3 1%
Other 7 3%
Unknown 81 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 18 February 2020.
All research outputs
#2,522,706
of 26,411,386 outputs
Outputs from arXiv
#40,996
of 1,015,729 outputs
Outputs of similar age
#46,782
of 339,205 outputs
Outputs of similar age from arXiv
#822
of 19,185 outputs
Altmetric has tracked 26,411,386 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,015,729 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done particularly well, scoring higher than 95% 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 339,205 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 86% of its contemporaries.
We're also able to compare this research output to 19,185 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 95% of its contemporaries.