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020 _a9783319105819
_9978-3-319-10581-9
024 7 _a10.1007/978-3-319-10581-9
_2doi
050 4 _aTA1637-1638
050 4 _aTA1637-1638
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082 0 4 _a006.6
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082 0 4 _a006.37
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100 1 _aWu, Guorong.
_eeditor.
245 1 0 _aMachine Learning in Medical Imaging
_h[electronic resource] :
_b5th International Workshop, MLMI 2014, Held in Conjunction with MICCAI 2014, Boston, MA, USA, September 14, 2014. Proceedings /
_cedited by Guorong Wu, Daoqiang Zhang, Luping Zhou.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXII, 332 p. 136 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v8679
505 0 _a 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.  .
520 _aThis 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.
650 0 _aComputer science.
650 0 _aMedical records
_xData processing.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aComputer vision.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aPattern Recognition.
650 2 4 _aHealth Informatics.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aComputer Graphics.
700 1 _aZhang, Daoqiang.
_eeditor.
700 1 _aZhou, Luping.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319105802
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v8679
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-10581-9
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