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 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.