Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings / edited by Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, Lena Maier-Hein, João Manuel R.S. Tavares, Andrew Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi.

Format:

Book

Contributor:

Stoyanov, Danail, Editor.

Taylor, Zeike, Editor.

Carneiro, Gustavo, Editor.

Syeda-Mahmood, Tanveer, Editor.

Martel, Anne, Editor.

Maier-Hein, Lena., Editor.

Tavares, João Manuel R.S., Editor.

Bradley, Andrew, Editor.

Papa, João Paulo, Editor.

Belagiannis, Vasileios., Editor.

Nascimento, Jacinto C., Editor.

Lü, Zhi, Editor.

Conjeti, Sailesh., Editor.

Moradi, Mehdi., Editor.

Greenspan, Hayit, Editor.

Madabhushi, Anant, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11045

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11045

Language:

English

Subjects (All):

Artificial intelligence.

Medical informatics.

Education-Data processing.

Social sciences-Data processing.

Data protection.

Artificial Intelligence.

Health Informatics.

Computers and Education.

Computer Application in Social and Behavioral Sciences.

Data and Information Security.

Local Subjects:

Artificial Intelligence.

Health Informatics.

Computers and Education.

Computer Application in Social and Behavioral Sciences.

Data and Information Security.

Physical Description:

1 online resource (XVII, 387 pages) : 197 illustrations, 149 illustrations in color.

Edition:

1st ed. 2018.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.

Contents:

Semi-Automated Extraction of Crohns Disease MR Imaging Markers using a 3D Residual CNN with Distance Prior

Weakly Supervised Localisation for Fetal Ultrasound Images

Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images

Segmentation of Head and Neck Organs-At-Risk in Longitudinal CT Scans Combining Deformable Registrations and Convolutional Neural Networks

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

Contextual Additive Networks to Efficiently Boost 3D Image Segmentations

Longitudinal detection of radiological abnormalities with time-modulated LSTM

SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy

Rapid Training Data Generation for Tissue Segmentation Using Global Approximate Block-Matching with Self-Organizing Maps

Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images

Deep semi-supervised segmentation with weight-averaged consistency targets

Focal Dice Loss and Image Dilation for Brain Tumor Segmentation

Automatic Detection of Patients with a High Risk of Systolic Cardiac Failure in Echocardiography

Unsupervised feature learning for outlier detection with stacked convolutional autoencoders, siamese networks and Wasserstein autoencoders: application to epilepsy detection

Automatic myocardial strain imaging in echocardiography using deep learning

3D Convolutional Neural Networks for Classification of Functional Connectomes

Computed Tomography Image Enhancement using 3D Convolutional Neural Network

Deep Particle Tracker: Automatic Tracking of Particles in Fluorescence Microscopy Images Using Deep Learning

A Unified Framework Integrating Recurrent Fully-convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data

Learning Optimal Deep Projection of 18 F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Learning to Segment Medical Images with Scribble-Supervision Alone

Unsupervised Probabilistic Deformation Modeling for Robust Diffeomorphic Registration

TreeNet: Multi-Loss Deep Learning Network to Predict Branch Direction for Extracting 3D Anatomical Trees

Active Deep Learning with Fisher Information for Patch-wise Semantic Segmentation

UOLO - automatic object detection and segmentation in biomedical images

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Multi-Scale Residual Network with Two Channels of Raw CT Image and Its Differential Excitation Component for Emphysema Classification

Nonlinear adaptively learned optimization for object localization in 3D medical images

Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

MTMR-Net: Multi-Task Deep Learning with Margin Ranking Loss for Lung Nodule Analysis

PIMMS: Permutation Invariant Multi-Modal Segmentation

Handling Missing Annotations for Semantic Segmentation with Deep ConvNets

3D Deep Affine-Invariant Shape Learning for Brain MR Image Segmentation

ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans

Unpaired Deep Cross-modality Synthesis with Fast Training

Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

Unpaired Brain MR-to-CT Synthesis using a Structure-Constrained CycleGAN

A Multi-Scale Multiple Sclerosis Lesion Change Detection in a Multi-Sequence MRI

Multi-task Sparse Low-rank Learning for Multi-classification of Parkinson's Disease

Optic Disc segmentation in Retinal Fundus Images using Fully Convolutional Network and Removal of False-positives Based on Shape Features

Integrating deformable modeling with 3D deep neural network segmentation.

Other Format:

Printed edition:

ISBN:

978-3-030-00889-5

9783030008895

Access Restriction:

Restricted for use by site license.