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020 _a9783642159480
_9978-3-642-15948-0
024 7 _a10.1007/978-3-642-15948-0
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aMachine Learning in Medical Imaging
_h[electronic resource] :
_bFirst International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings /
_cedited by Fei Wang, Pingkun Yan, Kenji Suzuki, Dinggang Shen.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aIX, 192 p. 84 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 ;
_v6357
505 0 _aFast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images -- Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries -- Content-Based Medical Image Retrieval with Metric Learning via Rank Correlation -- A Hyper-parameter Inference for Radon Transformed Image Reconstruction Using Bayesian Inference -- Patch-Based Generative Shape Model and MDL Model Selection for Statistical Analysis of Archipelagos -- Prediction of Dementia by Hippocampal Shape Analysis -- Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis -- Appearance Normalization of Histology Slides -- Parallel Mean Shift for Interactive Volume Segmentation -- Soft Tissue Discrimination Using Magnetic Resonance Elastography with a New Elastic Level Set Model -- Fast and Automatic Heart Isolation in 3D CT Volumes: Optimal Shape Initialization -- Relation-Aware Spreadsheets for Multimodal Volume Segmentation and Visualization -- A Bayesian Learning Application to Automated Tumour Segmentation for Tissue Microarray Analysis -- Generalized Sparse Classifiers for Decoding Cognitive States in fMRI -- Manifold Learning for Biomarker Discovery in MR Imaging -- Optimal Live Cell Tracking for Cell Cycle Study Using Time-Lapse Fluorescent Microscopy Images -- Fully Automatic Joint Segmentation for Computer-Aided Diagnosis and Planning -- Accurate Identification of MCI Patients via Enriched White-Matter Connectivity Network -- Feature Extraction for fMRI-Based Human Brain Activity Recognition -- Sparse Spatio-temporal Inference of Electromagnetic Brain Sources -- Optimal Gaussian Mixture Models of Tissue Intensities in Brain MRI of Patients with Multiple-Sclerosis -- Preliminary Study on Appearance-Based Detection of Anatomical Point Landmarks in Body Trunk CT Images -- Principal-Component Massive-Training Machine-Learning Regression for False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography.
520 _aThe first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient’s imaging data is often not sufficient to provide satisfactory performance; the- fore tasks in medical imaging require learning from examples to simulate a physician’s prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers.
650 0 _aComputer science.
650 0 _aRadiology.
650 0 _aAlgorithms.
650 0 _aArtificial intelligence.
650 0 _aComputer graphics.
650 0 _aImage processing.
650 0 _aPattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aImaging / Radiology.
650 2 4 _aPattern Recognition.
650 2 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
700 1 _aWang, Fei.
_eeditor.
700 1 _aYan, Pingkun.
_eeditor.
700 1 _aSuzuki, Kenji.
_eeditor.
700 1 _aShen, Dinggang.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642159473
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v6357
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-15948-0
912 _aZDB-2-SCS
912 _aZDB-2-LNC
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