000 06697nam a22006735i 4500
001 978-3-030-33327-0
003 DE-He213
005 20240730170457.0
007 cr nn 008mamaa
008 191012s2019 sz | s |||| 0|eng d
020 _a9783030333270
_9978-3-030-33327-0
024 7 _a10.1007/978-3-030-33327-0
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQV
_2thema
082 0 4 _a006.37
_223
245 1 0 _aMachine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting
_h[electronic resource] :
_bFirst International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings /
_cedited by Hongen Liao, Simone Balocco, Guijin Wang, Feng Zhang, Yongpan Liu, Zijian Ding, Luc Duong, Renzo Phellan, Guillaume Zahnd, Katharina Breininger, Shadi Albarqouni, Stefano Moriconi, Su-Lin Lee, Stefanie Demirci.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXVII, 212 p. 83 illus., 68 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11794
505 0 _aProceedings of the Machine Learning and Medical Engineering for Cardiovascular Health, MLMECH 2019 -- Arrhythmia Classification with Attention-Based ResBiLSTM-Net -- A Multi-Label Learning Method to detect Arrhythmia Based on -- An Ensemble Neural Network for Multi-label Classification of Electrocardiogram -- Automatic Diagnosis with 12-lead ECG Signals -- Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks -- Transfer Learning for Electrocardiogram Classification under Small Dataset -- Multi-label classification of abnormalities in 12-lead ECG using 1D CNN and LSTM -- An Approach to Predict Multiple Cardiac Diseases -- A 12-lead ECG Arrhythmia Classification Method Based on 1D Densely Connected CNN -- Automatic Multi-label Classification in 12-lead ECGs Using Neural Networks and Characteristic Points -- Automatic Detection of ECG Abnormalities by using an Ensemble of Deep Residual Networks with Attention -- Deep Learning toImprove Heart Disease Risk Prediction -- LabelECG: A Web-based Tool for Distributed Electrocardiogram Annotation -- Particle Swarm Optimization for Great Enhancement in Semi-Supervised Retinal Vessel Segmentation with Generative Adversarial Networks -- Attention-Guided Decoder in Dilated Residual Network for Accurate Aortic Valve Segmentation in 3D CT Scans -- ARVBNet: Real-time Detection of Anatomical Structures in Fetal Ultrasound Cardiac Four-chamber Planes -- Proceedings of the Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019 -- The Effect of Labeling Duration and Temporal Resolution on Arterial Transit Time Estimation Accuracy in 4D ASL MRA Datasets - a Flow Phantom Study -- Towards Quantifying Neurovascular Resilience -- Random 2.5D U-net for Fully 3D Segmentation -- Abdominal aortic aneurysm segmentation using convolutional neural networks trained with images generated with a synthetic shape model -- Tracking of intracavitary instrument markers in coronary angiography images -- Healthy Vessel Wall Detection Using U-Net in Optical Coherence Tomography -- Advanced Multi-objective Design Analysis to Identify Ideal Stent Design -- Simultaneous Intracranial Artery Tracing and Segmentation from Magnetic Resonance Angiography by Joint Optimization from Multiplanar Reformation.
520 _aThis book constitutes the refereed proceedings of the First International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For MLMECH 2019, 16 papers were accepted for publication from a total of 21 submissions. They focus on machine learning techniques and analyzing of ECG data in the diagnosis of heart diseases. CVII-STENT 2019 accepted all 8 submissiones for publication. They contain technological and scientific research concerning endovascular procedures. .
650 0 _aComputer vision.
_993796
650 0 _aArtificial intelligence.
_93407
650 1 4 _aComputer Vision.
_993800
650 2 4 _aArtificial Intelligence.
_93407
700 1 _aLiao, Hongen.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993803
700 1 _aBalocco, Simone.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993804
700 1 _aWang, Guijin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993805
700 1 _aZhang, Feng.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_921251
700 1 _aLiu, Yongpan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993807
700 1 _aDing, Zijian.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993808
700 1 _aDuong, Luc.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993809
700 1 _aPhellan, Renzo.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993810
700 1 _aZahnd, Guillaume.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993811
700 1 _aBreininger, Katharina.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993812
700 1 _aAlbarqouni, Shadi.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993813
700 1 _aMoriconi, Stefano.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993815
700 1 _aLee, Su-Lin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993816
700 1 _aDemirci, Stefanie.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_993817
710 2 _aSpringerLink (Online service)
_993822
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030333263
776 0 8 _iPrinted edition:
_z9783030333287
830 0 _aImage Processing, Computer Vision, Pattern Recognition, and Graphics,
_x3004-9954 ;
_v11794
_993823
856 4 0 _uhttps://doi.org/10.1007/978-3-030-33327-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cELN
999 _c86979
_d86979