Medical Image Computing and Computer Assisted Intervention - MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I / 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., 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 (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11764

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11764

Language:

English

Subjects (All):

Computer vision.

Pattern recognition systems.

Artificial intelligence.

Medical informatics.

Computer Vision.

Automated Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Local Subjects:

Computer Vision.

Automated Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Physical Description:

1 online resource (XXXVII, 819 pages) : 345 illustrations, 294 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

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:

Optical Imaging

Enhancing OCT Signal by Fusion of GANs: Improving Statistical Power of Glaucoma Trials

A Deep Reinforcement Learning Framework for Frame-by-frame Plaque Tracking on Intravascular Optical Coherence Tomography Image

Multi-Index Optic Disc Quantification via MultiTask Ensemble Learning

Retinal Abnormalities Recognition Using Regional Multitask Learning

Unifying Structure Analysis and Surrogate-driven Function Regression for Glaucoma OCT Image Screening

Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces

3D Surface-Based Geometric and Topological Quantification of Retinal Microvasculature in OCT-Angiography via Reeb Analysis

Limited-Angle Diffuse Optical Tomography Image Reconstruction using Deep Learning

Data-driven Enhancement of Blurry Retinal Images via Generative Adversarial Networks

Dual Encoding U-Net for Retinal Vessel Segmentation

A Deep Learning Design for improving Topology Coherence in Blood Vessel Segmentation

Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

Unsupervised Ensemble Strategy for Retinal Vessel Segmentation

Fully convolutional boundary regression for retina OCT segmentation

PM-NET: Pyramid Multi-Label Network for Optic Disc and Cup Segmentation

Biological Age Estimated from Retinal Imaging: A Novel Biomarker of Aging

Task Adaptive Metric Space for Medium-Shot Medical Image Classification

Two-Stream CNN with Loose Pair Training for Multi-modal AMD Categorization

Deep Multi Label Classification in Affine Subspaces

Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss

A Divide-and-Conquer Approach towards Understanding Deep Networks

Multiclass segmentation as multitask learning for drusen segmentation in retinal optical coherence tomography

Active Appearance Model Induced Generative Adversarial Networks for Controlled Data Augmentation

Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network

Probabilistic Atlases to Enforce Topological Constraints

Synapse-Aware Skeleton Generation for Neural Circuits

Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation

EDA-Net: Dense Aggregation of Deep and Shallow Information Achieves Quantitative Photoacoustic Blood Oxygenation Imaging Deep in Human Breast

Fused Detection of Retinal Biomarkers in OCT Volumes

Vessel-Net: Retinal Vessel Segmentation under Multi-path Supervision

Ki-GAN: Knowledge Infusion Generative Adversarial Network for Photoacoustic Image Reconstruction in vivo

Uncertainty guided semisupervised segmentation of retinal layers in OCT images

Endoscopy

Triple ANet: Adaptive Abnormal-aware Attention Network for WCE Image Classification

Selective Feature Aggregation Network with Area-boundary Constraints for Polyp Segmentation

Deep Sequential Mosaicking of Fetoscopic Videos

Landmark-guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection

Multi-View Learning with Feature Level Fusion for Cervical Dysplasia Diagnosis

Real-time Surface Deformation Recovery from Stereo Videos

Microscopy

Rectified Cross-Entropy and Upper Transition Loss for Weakly Supervised Whole Slide Image Classifier

From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification

Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes

Improving Nuclei/Gland Instance Segmentation in Histopathology Images by Full Resolution Neural Network and Spatial Constrained Loss

Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification

Cell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-Rate

Accelerated ML-assisted Tumor Detection in High-Resolution Histopathology Images

Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype

Pathology-aware deep network visualization and its application in glaucoma image synthesis

CORAL8: Concurrent Object Regression for Area Localization in Medical Image Panels

ET-Net: A Generic Edge-Attention Guidance Network for Medical Image Segmentation

Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints

Diverse Multiple Prediction on Neural Image Reconstruction

Deep Segmentation-Emendation Model for Gland Instance Segmentation

Fast and Accurate Electron Microscopy Image Registration with 3D Convolution

PlacentaNet: Automatic Morphological Characterization of Placenta Photos with Deep Learning

Deep Multi-Instance Learning for survival prediction from Whole Slide Images

High-Resolution Diabetic Retinopathy Image Synthesis Manipulated by Grading and Lesions

Deep Instance-Level Hard Negative Mining Model for Histopathology Images

Synthetic patches, real images: screening for centrosome aberrations in EM images of human cancer cells

Patch Transformer for Multi-tagging Whole Slide Histopathology Images

Pancreatic Cancer Detection in Whole Slide Images Using Noisy Label Annotations

Encoding histopathological WSIs using GNN for scalable diagnostically relevant regions retrieval

Local and Global Consistency Regularized Mean Teacher for Semi-supervised Nuclei Classification

Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer

Precise Separation of Adjacent Nuclei using a Siamese Neural Network

PFA-ScanNet: Pyramidal Feature Aggregation with Synergistic Learning for Breast Cancer Metastasis Analysis

DeepACE: Automated Chromosome Enumeration in Metaphase Cell Images Using Deep Convolutional Neural Networks

Unsupervised Subtyping of Cholangiocarcinoma Using A Deep Clustering Convolutional Autoencoder

Evidence Localization for Pathology Images using Weakly Supervised Learning

Nuclear Instance Segmentation using a Proposal-Free Spatially Aware Deep Learning Framework

GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy

IRNet: Instance Relation Network for Overlapping Cervical Cell Segmentation

Weakly Supervised Cell Segmentation in Dense by Propagating from Detection Map

Understanding Fixation in Fluorescence Microscopy via Robust Non-negative Tensor Factorization, Atlas-based Motion Correction and Functional Statistics

ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection, and Cell Classification in Multiplex Immunohistochemistry Images

Multi-task learning of a deep K-nearest neighbour network for histopathological image classification and retrieval

Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy images

Enhanced Cycle-Consistent Generative Adversarial Network for Color Normalization of H&E Stained Images

Nuclei Segmentation in Histopathological Images using Two-Stage Learning

ACE-Net: Biomedical Image Segmentation with Augmented Contracting and Expansive Paths

CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation

PseudoEdgeNet: Nuclei Segmentation only with Point Annotations

Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification

Progressive Learning for Neuronal Population Reconstruction from Optical Microscopy Images

Whole-Sample Mapping of Cancerous and Benign Tissue Properties

Multi-Task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification

Fine-Scale Vessel Extraction in Fundus Images by Registration with Fluorescein Angiography

DME-Net: Diabetic Macular Edema Grading by Auxiliary Task Learning

Attention Guided Network for Retinal Image Segmentation

An unsupervised domain adaptation approach to classification of stem cell-derived cardiomyocytes.

Other Format:

Printed edition:

ISBN:

978-3-030-32239-7

9783030322397

Access Restriction:

Restricted for use by site license.