Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part VI / edited by Dinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan.

Format:

Book

Contributor:

Shen, Dinggang, editor.

Liu, Tianming, editor.

Peters, Terry M., 1948 January 5- editor.

Staib, Lawrence H., editor.

Essert, Caroline, editor.

Zhou, Xiangyun Sean, editor.

Yap, Pew-Thian, editor.

Khan, Ali, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11769.

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11769

Language:

English

Subjects (All):

Optical data processing.

Pattern perception.

Artificial intelligence.

Medical informatics.

Image Processing and Computer Vision.

Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Local Subjects:

Image Processing and Computer Vision.

Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Physical Description:

1 online resource (XXXVIII, 860 pages) : 476 illustrations, 308 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.

Contents:

Computed Tomography

Multi-Scale Coarse-to-Fine Segmentation for Screening Pancreatic Ductal Adenocarcinoma

MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection

Spatial-Frequency Non-Local Convolutional LSTM Network for pRCC classification

binary coded decimal-Net for Low-dose CT Reconstruction: Acceleration, Convergence, and Generalization

Abdominal Adipose Tissue Segmentation in MRI with Double Loss Function Collaborative Learning

Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks

Generating Pareto optimal dose distributions for radiation therapy treatment planning

PAN: Projective Adversarial Network for Medical Image Segmentation

Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction

Multi-Class Gradient Harmonized Dice Loss with Application to Knee MR Image Segmentation

LSRC: A Long-Short Range Context-Fusing Framework for Automatic 3D Vertebra Localization

Contextual Deep Regression Network for Volume Estimation in Orbital CT

Multi-scale GANs for Memory-efficient Generation of High Resolution Medical Images

Deep Learning based Metal Artifacts Reduction in post-operative Cochlear Implant CT Imaging

ImHistNet: Learnable Image Histogram Based DNN with Application to Noninvasive Determination of Carcinoma Grades in CT Scans

DPA-DenseBiasNet: Semi-supervised 3D Fine Renal Artery Segmentation with Dense Biased Network and Deep Priori Anatomy

Semi-supervised Segmentation of Liver Using Adversarial Learning with Deep Atlas Prior

Pairwise Semantic Segmentation via Conjugate Fully Convolutional Network

Unsupervised Deformable Image Registration Using Cycle-Consistent CNN

Volumetric Attention for 3D Medical Image Segmentation and Detection

Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention

MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation

Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

AirwayNet: A Voxel-Connectivity Aware Approach for Accurate Airway Segmentation Using Convolutional Neural Networks

Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation

Bronchus Segmentation and Classification by Neural Networks and Linear Programming

Unsupervised Segmentation of Micro-CT Images of Lung Cancer Specimen Using Deep Generative Models

Normal appearance autoencoder for lung cancer detection and segmentation

mlVIRNET: Multilevel Variational Image Registration Network

NoduleNet: Decoupled False Positive Reduction for Pulmonary Nodule Detection and Segmentation

Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition

Targeting Precision with Data Augmented Samples in Deep Learning

Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images

Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster

Deep Variational Networks with Exponential Weighting for Learning Computed Tomography

R2-Net: Recurrent and Recursive Network for Sparse-view CT Artifacts Removal

Stereo-Correlation and Noise-Distribution Aware ResVoxGAN for Dense Slices Reconstruction and Noise Reduction in Thick Low-Dose CT

Harnessing 2D Networks and 3D Features for Automated Pancreas Segmentation from Volumetric CT Images

Tubular Structure Segmentation Using Spatial Fully Connected Network With Radial Distance Loss for 3D Medical Images

Bronchial Cartilage Assessment with Model-Based GAN Regressor

Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy

Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis

Automatically Localizing a Large Set of Spatially Correlated Key Points: A Case Study in Spine Imaging

Permutohedral Attention Module for Efficient Non-Local Neural Networks

Improving RetinaNet for CT Lesion Detection with Dense Masks from Weak RECIST Labels

X-ray Imaging

PRSNet: Part Relation and Selection Network for Bone Age Assessment

Mask Embedding for Realistic High-resolution Medical Image Synthesis

TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays

Misshapen Pelvis Landmark Detection by Spatial Local Correlation Mining for Diagnosing Developmental Dysplasia of the Hip

Adversarial Policy Gradient for Deep Learning Image Augmentation

Weakly Supervised ROI Mining Toward Universal Fracture Detection in Pelvic X-ray

Learning from Suspected Target: Bootstrapping Performance for Breast Cancer Detection in Mammography

From Unilateral to Bilateral Learning: Detecting Mammogram Mass with Contrasted Bilateral Network

Signed Laplacian Deep Learning with Adversarial Augmentation for Improved Mammography Diagnosis

Uncertainty measurements for the reliable classification of mammograms

GraphX$^{NET}-$ Chest X-Ray Classification Under Extreme Minimal Supervision

3DFPN-HS2: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

Automated detection and type classification of central venous catheters in chest X-rays

A Comprehensive Framework for Accurate Classification of Pulmonary Nodules

Hand Pose Estimation for Pediatric Bone Age Assessment

An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms

Learning-based X-ray Image Denoising utilizing Model-based Image Simulations

LVC-Net: Medical image segmentation with noisy label based on Local Visual Cues

Unsupervised Cone-Beam Computed Tomography (CBCT) segmentation based on adversarial learning domain adaptation

Pick-and-Learn: Automatic Quality Evaluation for Noisy-Labeled Image Segmentation

Anatomical Priors for Image Segmentation via Post-Processing with Denoising Autoencoders

Simultaneous Lung Field Detection and Segmentation for Pediatric ChestRadiographs

Deep Esophageal Clinical Target Volume Delineation using Encoded 3D Spatial Context of Tumor, Lymph Nodes, and Organs At Risk

Weakly Supervised Segmentation Framework with Uncertainty: A Study on Pneumothorax Segmentation in Chest X-ray

Multi-task Localization and Segmentation for X-ray Guided Planning in Knee Surgery

Towards fully automatic X-ray to CT registration

Adaptive image-feature learning for disease classification using inductive graph networks

How to learn from unlabeled volume data: Self-Supervised 3D Context Feature Learning

Probabilistic Radiomics: Ambiguous Diagnosis with Controllable Shape Analysis

Extract Bone Parts without Human Prior: End-to-end Convolutional Neural Network for Pediatric Bone Age Assessment

Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment

Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays

Medical-based Deep Curriculum Learning for Improved Fracture Classification

Realistic Breast Mass Generation through BIRADS Category

Learning from Longitudinal Mammography Studies

Automated Radiology Report Generation via Multi-view Image Fusion and Medical Concept Enrichment

Multi-label Thoracic Disease Image Classification with Cross-attention Networks

InfoMask: Masked Variational Latent Representation to Localize Chest Disease

Longitudinal Change Detection on Chest X-rays using Geometric Correlation Maps

Adversarial Pulmonary Pathology Translation for Pairwise Chest X-ray Data Augmentation

Semi-Supervised Learning by Disentangling and Self-Ensembling over Stochastic Latent Space

An Automated Cobb Angle Estimation Method Using Convolutional Neural Network with Area Limitation

Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data

Learning Interpretable Features via Adversarially Robust Optimization

Synthesize Mammogram from Digital Breast Tomosynthesis with Gradient Guided cGANs

Semi-supervised Medical Image Segmentation via Learning Consistency under Transformations

Improved Inference via Deep Input Transfer

Neural Architecture Search for Adversarial Medical Image Segmentation

MeshSNet: Deep Multi-Scale Mesh Feature Learning for End-to-End Tooth Labeling on 3D Dental Surfaces

Improving Robustness

of Medical Image Diagnosis with Denoising Convolutional Neural Networks.

Other Format:

Printed edition:

ISBN:

978-3-030-32226-7

9783030322267

Access Restriction:

Restricted for use by site license.