Atjaunināt sīkdatņu piekrišanu

E-grāmata: Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, Proceedings

Edited by , Edited by , Edited by , Edited by , Edited by , Edited by
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 53,52 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013, ProceedingsPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, in Nagoya, Japan, in September 2013. The 32 contributions included in this volume were carefully reviewed and selected from 57 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.
Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR
Images.- Integrating Multiple Network Properties for MCI Identification.-
Learning-Boosted Label Fusion for Multi-atlas Auto-Segmentation.- Volumetric
Segmentation of Key Fetal Brain Structures in 3D Ultrasound.- Sparse
Classification with MRI Based Markers for Neuromuscular Disease
Categorization.- Fully Automatic Detection of the Carotid Artery from
Volumetric Ultrasound Images Using Anatomical Position-Dependent LBP
Features.- A Transfer-Learning Approach to Image Segmentation Across Scanners
by Maximizing Distribution Similarity.- A New Algorithm of Electronic
Cleansing for Weak Faecal-Tagging CT Colonography.- A Unified Approach to
Shape Model Fitting and Non-rigid Registration.- A Bayesian Algorithm for
Image-Based Time-to-Event Prediction.- Patient-Specific Manifold Embedding of
Multispectral Images Using Kernel Combinations.- fMRI Analysis with Sparse
Weisfeiler-Lehman Graph Statistics.- Patch-Based Segmentation without
Registration: Application to Knee MRI.- Flow-Based Correspondence Matching in
Stereovision.- Thickness NETwork (ThickNet) Features for the Detection of
Prodromal AD.- Metric Space Structures for Computational Anatomy.-
Discriminative Group Sparse Representation for Mild Cognitive Impairment
Classification.- Temporally Dynamic Resting-State Functional Connectivity
Networks for Early MCI Identification.- An Improved Optimization Method for
the Relevance Voxel Machine.- Disentanglement of Session and Plasticity
Effects in Longitudinal fMRI Studies.- Identification of Alzheimers Disease
Using Incomplete Multimodal Dataset via Matrix Shrinkage and Completion.- On
Feature Relevance in Image-Based Prediction Models: An Empirical Study.-
Decision Forests with Spatio-Temporal Features for Graph-Based Tumor
Segmentation in 4D Lung CT.- Improving Probabilistic Image Registration via
Reinforcement Learning and Uncertainty Evaluation.- HEp-2 Cell Image
Classification: AComparative Analysis.- A 2.5D Colon Wall Flattening Model
for CT-Based Virtual Colonoscopy.- Augmenting Auto-context with Global
Geometric Features for Spinal Cord Segmentation.- Large-Scale Manifold
Learning Using an Adaptive Sparse Neighbor Selection Approach for Brain Tumor
Progression Prediction.- Ensemble Universum SVM Learning for Multimodal
Classification of Alzheimers Disease.- Joint Sparse Coding Spatial Pyramid
Matching for Classification of Color Blood Cell Image.- Multi-task Sparse
Classifier for Diagnosis of MCI Conversion to AD with Longitudinal MR
Images.- Sparse Multimodal Manifold-Regularized Transfer Learning for MCI
Conversion Prediction.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97833190226732e.html
Keywords: