Machine Learning in Medical Imaging : 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings /
| Corporate Author: | |
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| Other Authors: | , , , |
| Format: | eBook |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Edition: | 1st ed. 2017. |
| Series: | Image Processing, Computer Vision, Pattern Recognition, and Graphics ;
10541 |
| Subjects: |
Table of Contents:
- From Large to Small Organ Segmentation in CT Using Regional Context
- Motion Corruption Detection in Breast DCE-MRI
- Detection and Localization of Drosophila Egg Chambers in Microscopy Images
- Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-specific Coronary Calcium Scoring
- Atlas of Classifiers for Brain MRI Segmentation
- Dictionary Learning and Sparse Coding-based Denoising for High-Resolution Task Functional Connectivity MRI Analysis
- Yet Another ADNI Machine Learning Paper? Paving The Way Towards Fully-reproducible Research on Classification of Alzheimer's Disease
- Multi-Factorial Age Estimation from Skeletal and Dental MRI Volumes
- Automatic Classification of Proximal Femur Fractures Based on Attention Models
- Joint Supervoxel Classification Forest for Weakly-Supervised Organ Segmentation
- Accurate and Consistent Hippocampus Segmentation Through Convolutional LSTM and View Ensemble
- STAR: Spatio-Temporal Architecture for Super-Resolution in Low-Dose CT Perfusion
- Classification of Alzheimer's Disease by Cascaded Convolutional Neural Networks Using PET Images
- Finding Dense Supervoxel Correspondence of Cone-Beam Computed Tomography Images
- Multi-Scale Volumetric ConvNet with Nested Residual Connections for Segmentation of Anterior Cranial Base
- Feature Learning and Fusion of Multimodality Neuroimaging and Genetic Data for Multi-Status Dementia Diagnosis
- 3D Convolutional Neural Networks with Graph Refinement for Airway Segmentation Using Incomplete Data Labels
- Efficient Groupwise Registration for Brain MRI by Fast Initialization
- Sparse Multi-View Task-centralized Learning for ASD Diagnosis
- Inter-Subject Similarity Guided Brain Network Modelling for MCI Diagnosis
- Scalable and Fault Tolerant Platform for Distributed Learning on Private Medical Data
- Triple-Crossing 2.5D Convolutional Neural Network for Detecting Neuronal Arbours in 3D Microscopic Images
- Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity
- Gradient Boosted Trees for Corrective Learning
- Self-paced Convolutional Neural Network for Computer Aided Detection in Medical Imaging Analysis
- A Point Says a Lot: An Interactive Segmentation Method for MR Prostate via One-Point Labeling
- Collage CNN for Renal Cell Carcinoma Detection from CT
- Aggregating Deep Convolutional Features for Melanoma Recognition in Dermoscopy Images
- Localizing Cardiac Structures in Fetal Heart Ultrasound Video
- Deformable Registration Through Learning of Context-Specific Metric Aggregation
- Segmentation of Craniomaxillofacial Bony Structures from MRI with a 3D Deep-learning Based Cascade Framework
- 3D U-net with Multi-Level Deep Supervision: Fully Automatic Segmentation of Proximal Femur in 3D MR Images
- Indecisive Trees for Classification and Prediction of Knee Osteoarthritis
- Whole Brain Segmentation and Labeling from CT using synthetic MR Images
- Structural Connectivity Guided Sparse Effective Connectivity for MCI Identification
- Fusion of High-order and Low-order Effective Connectivity Networks for MCI Classification
- Novel Effective Connectivity Network Inference for MCI Identification
- Reconstruction of Thin-Slice Medical Images Using Generative Adversarial Network
- Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to "Virtual" High-Dose CT Images
- Deep-Fext: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction
- Product Space Decompositions for Continuous Representations of Brain Connectivity
- Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks
- Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
- Tversky Loss Function for Image Segmentation Using 3D Fully Convolutional Deep Networks.