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Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017

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Cover of 'Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017'

Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Deep Multi-task Multi-channel Learning for Joint Classification and Regression of Brain Status
  3. Altmetric Badge
    Chapter 2 Nonlinear Feature Space Transformation to Improve the Prediction of MCI to AD Conversion
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    Chapter 3 Kernel Generalized-Gaussian Mixture Model for Robust Abnormality Detection
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    Chapter 4 Latent Processes Governing Neuroanatomical Change in Aging and Dementia
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    Chapter 5 A Multi-armed Bandit to Smartly Select a Training Set from Big Medical Data
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    Chapter 6 Multi-level Multi-task Structured Sparse Learning for Diagnosis of Schizophrenia Disease
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    Chapter 7 An Unbiased Penalty for Sparse Classification with Application to Neuroimaging Data
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    Chapter 8 Unsupervised Feature Learning for Endomicroscopy Image Retrieval
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    Chapter 9 Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data
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    Chapter 10 Liver Tissue Classification in Patients with Hepatocellular Carcinoma by Fusing Structured and Rotationally Invariant Context Representation
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    Chapter 11 DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI
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    Chapter 12 Supervised Intra-embedding of Fisher Vectors for Histopathology Image Classification
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    Chapter 13 GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction
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    Chapter 14 MRI-Based Surgical Planning for Lumbar Spinal Stenosis
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    Chapter 15 Pattern Visualization and Recognition Using Tensor Factorization for Early Differential Diagnosis of Parkinsonism
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    Chapter 16 Physiological Parameter Estimation from Multispectral Images Unleashed
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    Chapter 17 Segmentation of Cortical and Subcortical Multiple Sclerosis Lesions Based on Constrained Partial Volume Modeling
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    Chapter 18 Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble
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    Chapter 19 Classification of Major Depressive Disorder via Multi-site Weighted LASSO Model
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    Chapter 20 A Multi-atlas Approach to Region of Interest Detection for Medical Image Classification
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    Chapter 21 Spectral Graph Convolutions for Population-Based Disease Prediction
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    Chapter 22 Predicting Future Disease Activity and Treatment Responders for Multiple Sclerosis Patients Using a Bag-of-Lesions Brain Representation
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    Chapter 23 Sparse Multi-kernel Based Multi-task Learning for Joint Prediction of Clinical Scores and Biomarker Identification in Alzheimer’s Disease
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    Chapter 24 Personalized Diagnosis for Alzheimer’s Disease
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    Chapter 25 GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
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    Chapter 26 Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans
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    Chapter 27 Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data
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    Chapter 28 Direct Detection of Pixel-Level Myocardial Infarction Areas via a Deep-Learning Algorithm
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    Chapter 29 Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images
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    Chapter 30 Boundary Regularized Convolutional Neural Network for Layer Parsing of Breast Anatomy in Automated Whole Breast Ultrasound
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    Chapter 31 Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection
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    Chapter 32 Full Quantification of Left Ventricle via Deep Multitask Learning Network Respecting Intra- and Inter-Task Relatedness
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    Chapter 33 Scalable Multimodal Convolutional Networks for Brain Tumour Segmentation
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    Chapter 34 Pathological OCT Retinal Layer Segmentation Using Branch Residual U-Shape Networks
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    Chapter 35 Quality Assessment of Echocardiographic Cine Using Recurrent Neural Networks: Feasibility on Five Standard View Planes
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    Chapter 36 Semi-supervised Deep Learning for Fully Convolutional Networks
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    Chapter 37 TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References
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    Chapter 38 BRIEFnet: Deep Pancreas Segmentation Using Binary Sparse Convolutions
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    Chapter 39 Supervised Action Classifier: Approaching Landmark Detection as Image Partitioning
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    Chapter 40 Robust Multi-modal MR Image Synthesis
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    Chapter 41 Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
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    Chapter 42 Clinical Target-Volume Delineation in Prostate Brachytherapy Using Residual Neural Networks
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    Chapter 43 Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography
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    Chapter 44 Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis
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    Chapter 45 Synergistic Combination of Learned and Hand-Crafted Features for Prostate Lesion Classification in Multiparametric Magnetic Resonance Imaging
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    Chapter 46 Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation
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    Chapter 47 Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images
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    Chapter 48 Medical Image Synthesis with Context-Aware Generative Adversarial Networks
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    Chapter 49 Joint Detection and Diagnosis of Prostate Cancer in Multi-parametric MRI Based on Multimodal Convolutional Neural Networks
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    Chapter 50 SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging
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    Chapter 51 X-Ray In-Depth Decomposition: Revealing the Latent Structures
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    Chapter 52 Fast Prospective Detection of Contrast Inflow in X-ray Angiograms with Convolutional Neural Network and Recurrent Neural Network
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    Chapter 53 Quantification of Metabolites in Magnetic Resonance Spectroscopic Imaging Using Machine Learning
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    Chapter 54 Building Disease Detection Algorithms with Very Small Numbers of Positive Samples
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    Chapter 55 Hierarchical Multimodal Fusion of Deep-Learned Lesion and Tissue Integrity Features in Brain MRIs for Distinguishing Neuromyelitis Optica from Multiple Sclerosis
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    Chapter 56 Deep Convolutional Encoder-Decoders for Prostate Cancer Detection and Classification
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    Chapter 57 Deep Image-to-Image Recurrent Network with Shape Basis Learning for Automatic Vertebra Labeling in Large-Scale 3D CT Volumes
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    Chapter 58 Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
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    Chapter 59 Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation
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    Chapter 60 Retinal Microaneurysm Detection Using Clinical Report Guided Multi-sieving CNN
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    Chapter 61 Lesion Detection and Grading of Diabetic Retinopathy via Two-Stages Deep Convolutional Neural Networks
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    Chapter 62 Hashing with Residual Networks for Image Retrieval
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    Chapter 63 Deep Multiple Instance Hashing for Scalable Medical Image Retrieval
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    Chapter 64 Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
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    Chapter 65 Discriminative Localization in CNNs for Weakly-Supervised Segmentation of Pulmonary Nodules
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    Chapter 66 Liver Lesion Detection Based on Two-Stage Saliency Model with Modified Sparse Autoencoder
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    Chapter 67 Manifold Learning of COPD
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    Chapter 68 Hybrid Mass Detection in Breast MRI Combining Unsupervised Saliency Analysis and Deep Learning
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    Chapter 69 Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
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    Chapter 70 Segmentation-Free Kidney Localization and Volume Estimation Using Aggregated Orthogonal Decision CNNs
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    Chapter 71 Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
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    Chapter 72 Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning
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    Chapter 73 CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
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    Chapter 74 Intra-perinodular Textural Transition (Ipris): A 3D Descriptor for Nodule Diagnosis on Lung CT
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    Chapter 75 Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT
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    Chapter 76 Deep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI
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    Chapter 77 Pancreas Segmentation in MRI Using Graph-Based Decision Fusion on Convolutional Neural Networks
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    Chapter 78 Modeling Cognitive Trends in Preclinical Alzheimer’s Disease (AD) via Distributions over Permutations
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    Chapter 79 Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?
Attention for Chapter 79: Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?
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Chapter title
Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?
Chapter number 79
Book title
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
Published in
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, September 2017
DOI 10.1007/978-3-319-66179-7_79
Pubmed ID
30035275
Book ISBNs
978-3-31-966178-0, 978-3-31-966179-7
Authors

Yinghuan Shi, Wanqi Yang, Yang Gao, Dinggang Shen, Shi, Yinghuan, Yang, Wanqi, Gao, Yang, Shen, Dinggang

Abstract

Prostate segmentation, for accurate prostate localization in CT images, is regarded as a crucial yet challenging task. Nevertheless, due to the inevitable factors (e.g., low contrast, large appearance and shape changes), the most important problem is how to learn the informative feature representation to distinguish the prostate from non-prostate regions. We address this challenging feature learning by leveraging the manual delineation as guidance: the manual delineation does not only indicate the category of patches, but also helps enhance the appearance of prostate. This is realized by the proposed cascaded deep domain adaptation (CDDA) model. Specifically, CDDA constructs several consecutive source domains by employing a mask of manual delineation to overlay on the original CT images with different mask ratios. Upon these source domains, convnet will guide better transferrable feature learning until to the target domain. Particularly, we implement two typical methods: patch-to-scalar (CDDA-CNN) and patch-to-patch (CDDA-FCN). Also, we theoretically analyze the generalization error bound of CDDA. Experimental results show the promising results of our method.

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Twitter Demographics

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 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 25%
Student > Doctoral Student 4 25%
Other 2 13%
Student > Master 2 13%
Researcher 1 6%
Other 0 0%
Unknown 3 19%
Readers by discipline Count As %
Computer Science 10 63%
Medicine and Dentistry 1 6%
Engineering 1 6%
Unknown 4 25%

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