E-raamat: 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

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 Lossfor 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 Parkinsons 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.