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Machine Learning in Medical Imaging

  • Kartonierter Einband
  • 344 Seiten
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This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014... Weiterlesen
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This book constitutes the refereed proceedings of the 5th International Workshop on Machine Learning in Medical Imaging, MLMI 2014, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014, in Cambridge, MA, USA, in September 2014. The 40 contributions included in this volume were carefully reviewed and selected from 70 submissions. They focus on major trends and challenges in the area of machine learning in medical imaging and aim to identify new cutting-edge techniques and their use in medical imaging.


Sparsity-Learning-Based Longitudinal MR Image Registration for Early Brain Development.- Graph-Based Label Propagation in Fetal brain MR Images.- Deep Learning Based Automatic immune Cell Detection for Immunohistochemistry Images.- Stacked Multiscale Feature learning for Domain Independent Medical Image Segmentation.- Detection of Mammographic Masses by Content-Based Image Retrieval.- Inferring Sources of Dementia Progression with Network Diffusion Model.- 3D Intervertebral Disc Localization through Representation Learning with Knowledge Transfer.- Exploring Compact Representation of SICE Matrices for Functional Brain Network Classification.- Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression.- Manifold Alignment and Transfer Learning for Classification of Alzheimer's Disease.- Gleason Grading of Prostate Tumors with Max-Margin Conditional Random Fields.- Learning Distance Transform for Boundary Detection and Deformable Segmentation in CT Prostate Images.- Geodesic Geometric mean of Regional Covariance Descriptors as an Image-Level Descriptor for nuclear Atypia Grading in Breast Images.- A constrained Regression Forests Solution to 3D Fetal Ultrasound Plane Localization for Longitudinal Analysis of Brain Growth and Maturation.- Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation.- Fetal Abdominal Standard Plane Localization through Representation Learning with Knowledge Transfer.- Searching for Structures of Interest in an Ultrasound Video Sequence.- Anatomically Constrained Weak Classifier Fusion for Early Detection of Alzheimer's Disease.- Automatic Bone and Marrow Extraction from Dual Energy CT through SVM Margin-Based Multi-Material Decomposition Model Selection.- Sparse Discriminative Feature Selection for Multi-Class Alzheimer's Disease Classification.- Context-aware Anatomical Landmark Detection: Application to Deformable Model Initialization in Prostate CT Images.- Optimal MAP Parameters Estimation in STAPLE-Learning from Performance Parameters versus Image Similarity Information.- Colon Biopsy Classification Using Crypt Architecture.- Network Guided Group Feature Selection for Classification of Autism Spectrum Disorder.- Deformation Field Correction for Spatial Normalization of PET Images Using a Population-derived Partial Least Squares Model.- Novel Multi-Atlas Segmentation by Matrix Completion.- Structured Random Forest for Myocardium Delineation in 3D Echocardiography.- Improved Reproducibility of Neuroanatomical Definition through Diffeomorphometry and Complexity Reduction.- Topological Descriptors of Histology Images.- Robust Deep Learning for Improved Classification of AD/MCI Patients.- Subject Specific Sparse Dictionary Learning for Atlas Based Brain MRI Segmentation.- Online Discriminative Multi-Atlas Learning with Application to Isointense Infant Brain Segmentation.


Titel: Machine Learning in Medical Imaging
Untertitel: 5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014, Proceedings
EAN: 9783319105802
ISBN: 3319105809
Format: Kartonierter Einband
Herausgeber: Springer International Publishing
Anzahl Seiten: 344
Gewicht: 522g
Größe: H235mm x B155mm x T18mm
Jahr: 2014
Untertitel: Englisch
Auflage: 2014

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