Medical Image Computing and Computer Assisted Intervention - MICCAI 2017

Name: Medical Image Computing and Computer Assisted Intervention - MICCAI 2017 | 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III
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20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III

Maxime Descoteaux Lena Maier-Hein Alfred Franz Pierre Jannin D. Louis Collins Simon Duchesne(Editor)

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Published on 3. September 2017

XXVI, 713 pages

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Content

Intro
Preface
Organization
Contents -- Part III
Feature Extraction and Classification Techniques
Deep Multi-task Multi-channel Learning for Joint Classification and Regression of Brain Status
1 Introduction
2 Materials and Methods
3 Experiments
4 Conclusion
References
Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion
1 Introduction
2 Method
2.1 Feature Transformation Through Coherent Point Drifting (CPD)
2.2 CPD Based LapSVM (CPD-LapSVM)
3 Experimental Results
3.1 AD vs. NC with MCI as Unknown
3.2 PMCI vs. sMCI with uMCI as Unknown
4 Conclusions
References
Kernel Generalized-Gaussian Mixture Model for Robust Abnormality Detection
1 Introduction and Related Work
2 Methods
2.1 Kernel Generalized Gaussian (KGG)
2.2 Kernel Generalized-Gaussian Mixture Model (KGGMM)
3 Results and Discussion
References
Latent Processes Governing Neuroanatomical Change in Aging and Dementia
1 Introduction
2 Method
2.1 Longitudinal Change in Brain Morphology
2.2 Latent Factor Model
3 Results
4 Conclusion
References
A Multi-armed Bandit to Smartly Select a Training Set from Big Medical Data
1 Introduction
1.1 Related Work
2 Method
2.1 Incremental Sample Selection
2.2 Multiple Partitions of the Source Data
2.3 Sample Selection as a Multi-armed Bandit Problem
3 Results
3.1 Data
3.2 Age Estimation
4 Conclusion
References
Multi-level Multi-task Structured Sparse Learning for Diagnosis of Schizophrenia Disease
1 Introduction
2 Method
3 Experiments
4 Conclusion
References
An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data
1 Introduction
2 Methods
2.1 Sparse Classification
2.2 Image-Based Penalty
2.3 SCAD and SCADTV Penalties
2.4 Optimization and Parameter Tuning
3 Experimental Results
3.1 Synthetic Data
3.2 Neuroimaging Data
3.3 Evaluation Methodology
3.4 Results
4 Discussion
References
Unsupervised Feature Learning for Endomicroscopy Image Retrieval
1 Introduction
2 Methodlogy
2.1 Building and Mining the Multimodal Graph
2.2 Discriminative Feature Learning
3 Experiments
3.1 Dataset and Experimental Settings
3.2 Results
4 Conclusion
References
Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data
1 Introduction
2 Method
2.1 Maximum Mean Discrepancy Based MKL
2.2 Subject Consistency
2.3 Joint Feature Selection and Classification
3 Experiments
3.1 Experiment Setting
3.2 Experimental Results
4 Conclusion
References
Liver Tissue Classification in Patients with Hepatocellular Carcinoma by Fusing Structured and Rotationally Invariant Context Representation
1 Introduction
2 Methods
2.1 Shape and Appearance Features
2.2 Label Context Features
3 Experiments
3.1 Data
3.2 Numerical Results and Discussion
4 Conclusion and Future Work
References
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI
1 Introduction
2 Method
2.1 Background
2.2 Dual Convolutional Filter Learning
2.3 Optimization
2.4 Synthesis
3 Experimental Results
4 Conclusion
References
Supervised Intra-embedding of Fisher Vectors for Histopathology Image Classification
1 Introduction
2 Methods
2.1 Fisher Vector
2.2 Supervised Intra-embedding
3 Results
4 Conclusions
References
GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction
1 Introduction
2 Method
2.1 GSplit LBI Algorithm
2.2 Setting the Parameters
3 Experimental Results
3.1 Prediction and Path Analysis
3.2 Lesion Features Analysis
4 Conclusions
References
MRI-Based Surgical Planning for Lumbar Spinal Stenosis
1 Introduction
2 Surgical Prediction from Numerical Dataset
3 Surgical Prediction from Radiological Images
4 Discussion
References
Pattern Visualization and Recognition Using Tensor Factorization for Early Differential Diagnosis of Parkinsonism
1 Introduction
2 Methods
2.1 Introduction to Tensor Factorization
2.2 Application of Tensor Factorization to 3D Images
3 Experiments and Results
4 Discussions and Conclusion
References
Physiological Parameter Estimation from Multispectral Images Unleashed
1 Introduction
2 Methods
2.1 Generic Approach to Physiological Parameter Estimation in Multispectral Imaging
2.2 Domain Adaptation
3 Experiments and Results
3.1 Experimental Setup
3.2 Validity of Tissue Model
3.3 Performance of Domain Adaptation
4 Discussion
References
Segmentation of Cortical and Subcortical Multiple Sclerosis Lesions Based on Constrained Partial Volume Modeling
1 Introduction
2 Method
2.1 Partial Volume Estimation
2.2 Bitmap of Lesion Location
2.3 Imaging Parameters
2.4 Hyperparameter Tuning
3 Experimental Validation
3.1 Data and Pre-processing
3.2 Results
4 Conclusion
References
Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble
1 Introduction
2 Data Acquisition
3 Method
3.1 Quantitative Features and Random Forest
3.2 CNN
3.3 Ensemble
4 Results and Discussion
5 Conclusion and Future Work
References
Outline placeholder
Abstract
1 Introduction
2 Methods
2.1 Data and Demographics
2.2 Data Preprocessing
2.3 Algorithm Overview
2.4 Ordinary LASSO and Weighted LASSO
2.5 Generation of a Multi-site Weight
2.6 Multi-site Weight LASSO
3 Results
3.1 Classification Improvements Through the MSW-LASSO Model
3.2 Analysis of MSW-LASSO Features
3.3 Reproducibility of the MSW-LASSO
4 Conclusion and Discussion
References
A Multi-atlas Approach to Region of Interest Detection for Medical Image Classification
1 Introduction
2 Multi-atlas ROI Detection for Image Classification
2.1 Problem Definition for ROI Detection
2.2 Multi-atlas Image Classification
2.3 Multi-atlas ROI Detection
2.4 ROI-Based Image Classification
3 Experiments
3.1 Data Description
3.2 Experiment Setup
3.3 Implementation Details
3.4 Results
4 Conclusions and Discussion
References
Spectral Graph Convolutions for Population-Based Disease Prediction
1 Introduction
2 Methods
2.1 Databases and Preprocessing
2.2 Population Graph Construction
2.3 Graph Labelling Using Graph Convolutional Neural Networks
3 Results
4 Discussion
References
Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients Using a Bag-of-Lesions Brain Representation
1 Introduction
2 Proposed Method
2.1 Activity Prediction
2.2 Identifying Responders to Treatment
3 Experiments and Results
3.1 Disease Activity Prediction
3.2 Responder Identification
4 Conclusion
References
Sparse Multi-kernel Based Multi-task Learning for Joint Prediction of Clinical Scores and Biomarker Identification in Alzheimer's Disease
1 Introduction
2 Sparse Multi Kernel Multi-task Learning, SMKMTL
3 Experimental Results
3.1 Data and Experimental Setting
3.2 Fusion of Multi-modality
4 Conclusions
References
Machine Learning in Medical Image Computing
Outline placeholder
Abstract
1 Introduction
2 Methods
2.1 Generalized Classification Model
2.2 Personalized Classification Model
2.3 Advance Personalized AD Diagnosis Model
3 Experiments
4 Conclusion
References
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
1 Introduction
2 Methods
2.1 Preprocessing
2.2 3D Regression Fully Convolutional Network
3 Experiments
4 Discussion
5 Conclusion
References
Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
1 Introduction
2 Approach
2.1 Formulation
2.2 Optimization
3 Experiments
3.1 Dataset and Evaluation
3.2 Results
4 Conclusions
References
Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
1 Introduction
2 Method
2.1 SD-Net for Image Segmentation
2.2 Fine-Tuning with Error Corrective Boosting
3 Results
4 Conclusion
References
Direct Detection of Pixel-Level Myocardial Infarction Areas via a Deep-Learning Algorithm
1 Introduction
2 Methodology
3 Experimental Results
4 Conclusions
References
Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images
1 Introduction
2 Methods
2.1 Cross-Modality DCNN Learning
2.2 Saliency Feature Learning
3 Experiments
3.1 Analysis of Cross-Modality Learning
3.2 Results with CAM-BP
3.3 Comparative Study and Other Detection Tasks
4 Conclusion
References
Boundary Regularized Convolutional Neural Network for Layer Parsing of Breast Anatomy in Automated W ...
Abstract
1 Introduction
2 Method
2.1 Our Mainstream ConvEDNet (MConvEDNet)
2.2 Two-Stages Domain Transfer (2DT)
2.3 Deep Boundary Supervision (DBS)
2.4 Implementation Details
3 Dataset and Annotation
4 Experiments and Results
5 Discussion and Conclusion
Acknowledgement
References
Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
1 Introduction
2 Architecture of Zoom-in-Net
3 Attention Localization Evaluation and Understanding
4 Quantitative Evaluation
5 Conclusions
References
Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness
1 Introduction
2 Multitask Learning for Full Quantification of Cardiac LV
2.1 Architectures of FullLVNet
2.2 Intra-task and Inter-task Relatedness
3 Dataset and Configurations
4 Results and Analysis
5 Conclusions
References
Scalable Multimodal Convolutional Networks for Brain Tumour Segmentation
1 Introduction
2 Structural Transformations Across Features/modalities
3 ScaleNets Implementation
4 Experiments and Results
5 Conclusions
References
Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks
1 Introduction
2 Methods
2.1 Branch Residual U-Network
2.2 Training
3 Experimental Results
4 Conclusion
References
Quality Assessment of Echocardiographic Cine Using Recurrent Neural Networks: Feasibility on Five Standard View Planes
1 Introduction
2 Materials and Method
2.1 Dataset and Gold Standard Quality Assessment
2.2 Network Architecture
2.3 Training
3 Experiments and Results
4 Discussion and Conclusion
References
Semi-supervised Deep Learning for Fully Convolutional Networks
1 Introduction
2 Methodology
2.1 Auxiliary Manifold Embedding
2.2 Random Feature Embedding
3 Experiments and Results
3.1 Baseline Models
3.2 Semi-supervised Embedding
4 Discussion and Conclusion
References
TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
1 Introduction
2 Method
3 Experiments
4 Conclusion
References
BRIEFnet: Deep Pancreas Segmentation Using Binary Sparse Convolutions
1 Introduction
2 Method
3 Experiments and Results
4 Discussion and Conclusion
References
Supervised Action Classifier: Approaching Landmark Detection as Image Partitioning
Abstract
1 Introduction
2 Theory
2.1 Landmark Representation Based on Action Map
2.2 Deep Image-to-Image Network Learning for Action Map Estimation
2.3 Action Map Aggregation for Landmark Detection
3 Methods and Results
3.1 Data
3.2 Experimental Setup
3.3 Qualitative and Quantitative Results
4 Discussion
References
Robust Multi-modal MR Image Synthesis
1 Introduction
2 Previous Work
3 Proposed Approach
3.1 Details
4 Cost Function
5 Experiments
5.1 Synthesis Accuracy
5.2 Multi-input Synthesis, and Robustness to Missing Inputs
5.3 Robustness to Data Misalignment
5.4 Transfer Learning
6 Conclusion
References
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
1 Introduction
2 Methods
2.1 Architecture
2.2 Deep Supervision and Objective Function
2.3 Dropout in Residual Networks
3 Dataset, Preprocessing and Network Training
4 Results and Discussion
5 Conclusion
References
Clinical Target-Volume Delineation in Prostate Brachytherapy Using Residual Neural Networks
1 Introduction
2 Methods
2.1 Materials
2.2 Network Architecture
2.3 Training and Loss Function
3 Evaluation
4 Results
5 Discussion and Conclusion
References
Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalog ...
Abstract
1 Introduction
2 Methods
2.1 Data Collection and Processing
2.2 Recognized CNN Models/Model Selection
2.3 Cross-Validation/Test Set
3 Results
4 Conclusion
Acknowledgements
References
Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis
1 Introduction
2 Saliency Map Calculation
3 Generative Adversarial Networks
3.1 Loss Function
4 Experiments and Results
4.1 Image Super Resolution Results
4.2 Retinal Blood Vessel Segmentation Results
5 Conclusion
References
Synergistic Combination of Learned and Hand-Crafted Features for Prostate Lesion Classification in Multiparametric Magnetic Resonance Imaging
1 Introduction
2 Materials and Methods
2.1 Data
2.2 Deep Learning Features Derived from CNN
2.3 Hand-Crafted Statistical Features
2.4 Texture Features Learned Using Discriminative Dictionaries
2.5 Combining Different Types of Features
3 Results and Discussion
4 Conclusion
References
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
1 Introduction
2 Method
2.1 A New Fully Convolutional Network
2.2 Uncertainty Estimation and Similarity Estimation
2.3 Annotation Suggestion
3 Experiments and Results
4 Conclusions
References
Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images
1 Introduction
2 Method
2.1 Adversarial Networks Using Unannotated Data
2.2 Constructing the Input of the Evaluation Network
3 Experiments and Results
4 Conclusions
References
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
1 Introduction
2 Methods
2.1 Proposed Supervised Generative Adversarial Networks (GAN)
2.2 Auto-Context Model (ACM) for Refinement
3 Experiments and Results
4 Conclusions
References
Joint Detection and Diagnosis of Prostate Cancer in Multi-parametric MRI Based on Multimodal Convolutional Neural Networks
1 Introduction
2 Our Method
2.1 Weakly-Supervised Multimodal CNNs
2.2 Post-processing
3 Experiment Results
3.1 Experimental Setup
3.2 Results
4 Conclusion
References
SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging
1 Introduction
2 Background
3 Proposed Stain Deconvolution Layer (SD-Layer)
4 Experiments
4.1 Dataset
4.2 Experiment 1: AlexNet vs T-CNN with and Without SD-Layer
4.3 Experiment 2: Results with Different Initializations of SD-Layer
5 Discussion
6 Conclusion
References
X-Ray In-Depth Decomposition: Revealing the Latent Structures
1 Introduction
2 Methodology
2.1 Problem Formulation
3 Experiments and Results
3.1 Application-Specific Model
3.2 Clinical Use-Case
4 Discussion and Conclusion
References
Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network
1 Introduction
2 Methods
2.1 The CNN-Based Method
2.2 The RNN-Based Method
3 Experiments
4 Results and Discussion
References
Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging Using Machine Learning
1 Introduction
2 Methods
3 Experiments and Results
3.1 Data
3.2 Results
4 Conclusion
References
Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
1 Introduction
2 Methodology
2.1 Network Architecture and Training
2.2 Feature Combinations for Classification Network
2.3 Data
3 Experiments and Results on Cardiac CT Data
3.1 Segmentation
3.2 Disease Detection
4 Conclusion
References
Hierarchical Multimodal Fusion of Deep-Learned Lesion and Tissue Integrity Features in Brain MRIs for Distinguishing Neuromyelitis Optica from Multiple Sclerosis
1 Introduction
2 Materials and Preprocessing
3 Methods
4 Experimental Results
5 Conclusion
References
Deep Convolutional Encoder-Decoders for Prostate Cancer Detection and Classification
1 Introduction
2 Methods
2.1 Data Preparation
2.2 Network Design and Training
3 Results
4 Conclusions and Future Work
References
Deep Image-to-Image Recurrent Network with Shape Basis Learning for Automatic Vertebra Labeling in Large-Scale 3D CT Volumes
1 Introduction
2 Method
2.1 Deep Image-to-Image Network (DI2IN)
2.2 Response Enhancement Using Multi-layer ConvLSTM
2.3 Shape Basis Network for Refinement
3 Experiments
4 Conclusion
References
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
1 Introduction
2 Methodology
2.1 Deep Image-to-Image Network (DI2IN) for Liver Segmentation
2.2 Network Improvement with Adversarial Training
3 Experiments
4 Conclusion
References
Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
1 Introduction
2 Materials and Method
2.1 Dataset
2.2 Sampling
2.3 Network Architecture and Training
2.4 Domain Adaptation
2.5 Experiments
3 Results
4 Discussion and Conclusions
References
Retinal Microaneurysm Detection Using Clinical Report Guided Multi-Sieving CNN
1 Introduction
2 Methodology
2.1 Learning Image-Text Mapping Model
2.2 Multi-Sieving Convolutional Neural Network for MA Detection
3 Experimental Results
4 Conclusions
References
Lesion Detection and Grading of Diabetic Retinopathy via Two-Stages Deep Convolutional Neural Networks
1 Introduction
2 Methods
3 Experimental Evaluation
3.1 Data Preparation
3.2 The Identification of Lesions
3.3 Grading the DR Severity of Fundus Images
4 Conclusion
References
Hashing with Residual Networks for Image Retrieval
1 Introduction
2 Methodology
2.1 Architecture for Deep Residual Hashing
2.2 Model Learning and Optimization
3 Experiments
4 Results and Discussion
5 Conclusions
References
Deep Multiple Instance Hashing for Scalable Medical Image Retrieval
1 Introduction
2 Methodology
3 Experiments
4 Results and Discussion
5 Conclusion
References
Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
1 Introduction
2 The Proposed CAD System
2.1 Candidate Detection Using Improved Faster R-CNN
2.2 False Positive Reduction Using 3D DCNN
3 Experimental Results and Discussions
3.1 Candidate Detection Results
3.2 False Positive Reduction Results
4 Conclusion
References
Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules
1 Introduction
2 Method
2.1 Nodule Activation Map
2.2 Segmentation
3 Experimental Results
3.1 Data and Experimental Setup
3.2 Segmentation Performance
4 Discussions and Conclusions
References
Liver Lesion Detection Based on Two-Stage Saliency Model with Modified Sparse Autoencoder
1 Introduction
2 Method
2.1 Multi-scale Patch Presentation
2.2 Patch Saliency Based on Gray Level Contrast
2.3 Patch Saliency Based on Feature Contrast
2.4 Final Saliency Construction
3 Results
4 Conclusion
References
Manifold Learning of COPD
1 Introduction
2 Method
2.1 Lung Deformation and Tissue Classification
2.2 Local Disease and Deformation Distributions
2.3 Manifold Learning of COPD Distributions
3 Experiments
3.1 Data Processing
3.2 Associations with Disease Severity
3.3 Trajectories of Emphysema and fSAD Progression
4 Discussion and Conclusion
References
Hybrid Mass Detection in Breast MRI Combining Unsupervised Saliency Analysis and Deep Learning
Abstract
1 Introduction
2 Methods
2.1 Datasets
2.2 Image Representation
2.3 Lesion Detection Framework
2.4 Experiments
3 Results
4 Discussion
References
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
1 Introduction
2 Deep MIL for Whole Mammogram Mass Classification
2.1 Max Pooling-Based Multi-instance Learning
2.2 Label Assignment-Based Multi-instance Learning
2.3 Sparse Multi-instance Learning
3 Experiments
4 Conclusion
References
Segmentation-Free Kidney Localization and Volume Estimation Using Aggregated Orthogonal Decision CNNs
1 Introduction
2 Materials and Methods
2.1 Data
2.2 Kidney Localization
2.3 Segmentation-Free Volume Estimation
3 Results
4 Conclusions
References
Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
1 Introduction
2 Methods
3 Experiments
4 Conclusion
References
Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
1 Introduction
2 Method
2.1 3D FCN with Online Sample Filtering for Candidate Screening
2.2 Hybrid-Loss 3D Residual Learning for False Positive Reduction
3 Experimental Results
4 Conclusion
References
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
1 Introduction
2 Method
3 Data and Implementation
4 Experiments and Results
5 Conclusions
References
Intra-perinodular Textural Transition (Ipris): A 3D Descriptor for Nodule Diagnosis on Lung CT
1 Introduction
2 Brief Overview and Novel Contributions
3 Methodology
3.1 Shell Definition
3.2 Ipris Features
4 Experimental Results and Discussion
4.1 Data Description and Pre-processing
4.2 Experiment 1: Evaluating Ipris via Support Vector Machine Classifiers
4.3 Experiment 2: Evaluating Ipris Against Established Textural and Shape Features
4.4 Experiment 3: Human-Machine Comparison
5 Conclusion
References
Outline placeholder
Abstract
1 Introduction
2 Data and Materials
3 Algorithm
3.1 Preprocessing and Data Augmentation
3.2 TMME for Nodule Slice Classification
3.3 Nodule Classification
4 Results
5 Discussion
5.1 Data Argumentation
5.2 Ensemble Learning
5.3 Other Pre-trained DCNN Models
5.4 Hybrid Ensemble of 27 TMME Models
5.5 Computational Complexity
6 Conclusion
Acknowledgments
References
Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI
1 Introduction
2 Literature Review
3 Methodology
3.1 Dataset
3.2 Training
3.3 Inference
4 Experiments
4.1 Results
5 Discussion and Conclusion
References
Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks
1 Introduction
2 Method
2.1 Contextual Regularization
2.2 Jaccard Loss
3 Experimental Results and Analysis
4 Conclusion
References
Modeling Cognitive Trends in Preclinical Alzheimer's Disease (AD) via Distributions over Permutations
1 Introduction
2 Identifying Patterns in Sets of Distributions over Sn
3 Experimental Evaluations
4 Conclusions
References
Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?
1 Introduction
2 Our Method
3 Experimental Results
References
Correction to: Retinal Microaneurysm Detection Using Clinical Report Guided Multi-Sieving CNN
Correction to: Chapter "Retinal Microaneurysm Detection Using Clinical Report Guided Multi-Sieving CNN" in: M. Descoteaux et al. (Eds.): Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, LNCS 10435, https://doi.org/10.1007/978-3-319-66179-7_60
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Medical Image Computing and Computer Assisted Intervention - MICCAI 2017

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