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020			_a3030885526
024	7		_a10.1007/978-3-030-88552-6 _2doi
035			_a(CKB)4100000012037908
035			_a(MiAaPQ)EBC6737923
035			_a(Au-PeEL)EBL6737923
035			_a(OCoLC)1272989641
035			_a(PPN)25805204X
035			_a(BIP)81776933
035			_a(BIP)81480639
035			_a(DE-He213)978-3-030-88552-6
035			_a(EXLCZ)994100000012037908
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ
041	0		_aeng
060			_aWN 180 _bM149 2021
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
096			_aWN 180 _bM149 2021EBK
245	1	0	_aMachine Learning for Medical Image Reconstruction : _b4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / _cedited by Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_a1 online resource (147 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12964
500			_aIncludes index.
505	0		_aDeep Learning for Magnetic Resonance Imaging -- HyperRecon: Regularization-Agnostic CS-MRI Reconstruction with Hypernetworks -- Efficient Image Registration Network For Non-Rigid Cardiac Motion Estimation -- Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge -- Self-Supervised Dynamic MRI Reconstruction -- A Simulation Pipeline to Generate Realistic Breast Images For Learning DCE-MRI Reconstruction -- Deep MRI Reconstruction with Generative Vision Transformers -- Distortion Removal and Deblurring of Single-Shot DWI MRI Scans -- One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline -- Physics-informed self-supervised deep learning reconstruction for accelerated rst-pass perfusion cardiac MRI -- Deep Learning for General Image Reconstruction -- Noise2Stack: Improving Image Restoration by Learning from Volumetric Data -- Real-time Video Denoising in Fluoroscopic Imaging -- A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution -- Semi- and Self-Supervised Multi-View Fusion of 3D Microscopy Images using Generative Adversarial Networks.
520			_aThis book constitutes the refereed proceedings of the 4th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2021, held in conjunction with MICCAI 2021, in October 2021. The workshop was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 papers presented were carefully reviewed and selected from 20 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging and deep learning for general image reconstruction.
650		0	_aArtificial intelligence.
650	1	4	_aArtificial Intelligence.
655		7	_aElectronic books _2local _92032
700	1		_aHaq, Nandinee, _eeditor
776	0	8	_z3-030-88551-8
830		0	_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12964
856	4	0	_3Springerlink _uhttps://link.springer.com/book/10.1007/978-3-030-88552-6 _zOnline access link to the resource
942			_2lcc _cEBK
999			_c200467731 _d85943