LEADER 07169nam 22008295 450 001 996465876403316 005 20200705013246.0 010 $a1-280-38927-3 010 $a9786613567192 010 $a3-642-15948-6 024 7 $a10.1007/978-3-642-15948-0 035 $a(CKB)2670000000045125 035 $a(SSID)ssj0000446613 035 $a(PQKBManifestationID)11298841 035 $a(PQKBTitleCode)TC0000446613 035 $a(PQKBWorkID)10497032 035 $a(PQKB)11369187 035 $a(DE-He213)978-3-642-15948-0 035 $a(MiAaPQ)EBC3065848 035 $a(PPN)149025076 035 $a(EXLCZ)992670000000045125 100 $a20100910d2010 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMachine Learning in Medical Imaging$b[electronic resource] $eFirst International Workshop, MLMI 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010, Proceedings /$fedited by Fei Wang, Pingkun Yan, Kenji Suzuki, Dinggang Shen 205 $a1st ed. 2010. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2010. 215 $a1 online resource (IX, 192 p. 84 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v6357 300 $aInternational conference proceedings. 311 $a3-642-15947-8 320 $aIncludes bibliographical references and author index. 327 $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. 330 $aThe first International Workshop on Machine Learning in Medical Imaging, MLMI 2010, was held at the China National Convention Center, Beijing, China on Sept- ber 20, 2010 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2010. Machine learning plays an essential role in the medical imaging field, including image segmentation, image registration, computer-aided diagnosis, image fusion, ima- guided therapy, image annotation, and image database retrieval. With advances in me- cal imaging, new imaging modalities, and methodologies such as cone-beam/multi-slice CT, 3D Ultrasound, tomosynthesis, diffusion-weighted MRI, electrical impedance to- graphy, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Single-sample evidence provided by the patient?s imaging data is often not sufficient to provide satisfactory performance; the- fore tasks in medical imaging require learning from examples to simulate a physician?s prior knowledge of the data. The MLMI 2010 is the first workshop on this topic. The workshop focuses on major trends and challenges in this area, and works to identify new techniques and their use in medical imaging. Our goal is to help advance the scientific research within the broad field of medical imaging and machine learning. The range and level of submission for this year's meeting was of very high quality. Authors were asked to submit full-length papers for review. A total of 38 papers were submitted to the workshop in response to the call for papers. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v6357 606 $aArtificial intelligence 606 $aOptical data processing 606 $aRadiology 606 $aPattern recognition 606 $aAlgorithms 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aImaging / Radiology$3https://scigraph.springernature.com/ontologies/product-market-codes/H29005 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 607 $aPeking <2010>$2swd 608 $aKongress.$2swd 615 0$aArtificial intelligence. 615 0$aOptical data processing. 615 0$aRadiology. 615 0$aPattern recognition. 615 0$aAlgorithms. 615 14$aArtificial Intelligence. 615 24$aImage Processing and Computer Vision. 615 24$aImaging / Radiology. 615 24$aPattern Recognition. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a616.07/54 702 $aWang$b Fei$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYan$b Pingkun$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSuzuki$b Kenji$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aShen$b Dinggang$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aMLMI 2010 906 $aBOOK 912 $a996465876403316 996 $aMachine Learning in Medical Imaging$91918862 997 $aUNISA LEADER 00772nam0-2200289 --450 001 9910908100203321 005 20250114091302.0 020 $aIT$b76174 100 $a20241129d1975----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $a<>nostro futuro$ei dilemmi ecologici dell'umanità$fAnnibale Pizzi 210 $aFirenze$cBonechi$d1975 215 $a282 p.$d21 cm 610 0 $aSocietà$aSec. 20. 610 0 $aFuturologia 676 $a309.1$v17 676 $a309.1$v18 700 1$aPizzi,$bAnnibale$0557308 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910908100203321 952 $aDAM A70 PIZA 01$b2024/8532$fFLFBC 959 $aFLFBC 996 $aNostro futuro$9929622 997 $aUNINA