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008			210929s2021 sz | o |||| 0|eng d
020			_a3030882101
024	7		_a10.1007/978-3-030-88210-5 _2doi
035			_a(CKB)4100000012037949
035			_a(MiAaPQ)EBC6737964
035			_a(Au-PeEL)EBL6737964
035			_a(OCoLC)1272992911
035			_a(PPN)258052007
035			_a(BIP)81776914
035			_a(BIP)81403753
035			_a(DE-He213)978-3-030-88210-5
035			_a(EXLCZ)994100000012037949
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ
041	0		_aeng
060			_aWN 180 _bD311 2011
072		7	_aUYT _2bicssc
072		7	_aCOM016000 _2bisacsh
072		7	_aUYT _2thema
096			_aWN 180 _bD311 2011EBK
245	1	0	_aDeep Generative Models, and Data Augmentation, Labelling, and Imperfections : _bFirst Workshop, DGM4MICCAI 2021, and First Workshop, DALI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings / _cedited by Sandy Engelhardt, Ilkay Oksuz, Dajiang Zhu, Yixuan Yuan, Anirban Mukhopadhyay, Nicholas Heller, Sharon Xiaolei Huang, Hien Nguyen, Raphael Sznitman, Yuan Xue.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_a1 online resource (285 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 ; _v13003
504			_aIncludes bibliographical references and index.
505	0		_aDGM4MICCAI 2021 - Image-to-Image Translation, Synthesis -- Frequency-Supervised MRI-to-CT Image Synthesis -- Ultrasound Variational Style Transfer to Generate Images Beyond the Observed Domain -- 3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images -- Bridging the gap between paired and unpaired medical image translation -- Conditional generation of medical images via disentangled adversarial inference. -CT-SGAN: Computed Tomography Synthesis GAN -- Hierarchical Probabilistic Ultrasound Image Inpainting via Variational Inference -- CaCL: class-aware codebook learning for weakly supervised segmentation on diffuse image patterns -- BrainNetGAN: Data augmentation of brain connectivity using generative adversarial network for dementia classification -- Evaluating GANs in medical imaging -- DGM4MICCAI 2021 - AdaptOR challenge -- Improved Heatmap-based Landmark Detection -- Cross-domain Landmarks Detection in Mitral Regurgitation -- DALI2021 -- Scalable Semi-supervised Landmark Localization for X-ray Images using Few-shot Deep Adaptive Graph -- Semi-supervised Surgical Tool Detection Based on Highly Confident Pseudo Labeling and Strong Augmentation Driven Consistency -- One-shot Learning for Landmarks Detection -- Compound Figure Separation of Biomedical Images with Side Loss -- Data Augmentation with Variational Autoencoders and Manifold Sampling -- Medical image segmentation with imperfect 3D bounding boxes -- Automated Iterative Label Transfer Improves Segmentation of Noisy Cells in Adaptive Optics Retinal Images -- How Few Annotations are Needed for Segmentation using a Multi-planar U-Net? -- FS-Net: A New Paradigm of Data Expansion for Medical Image Segmentation -- An Efficient Data Strategy for the Detection of Brain Aneurysms from MRA with Deep Learning -- Evaluation of Active Learning Techniques on Medical Image Classification with Unbalanced Data Distributions -- Zero-Shot Domain Adaptation in CT Segmentation by Filtered Back Projection Augmentation -- Label Noise in Segmentation Networks : Mitigation Must Deal with Bias -- DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization -- MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation.
520			_aThis book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorousstudy of medical data related to machine learning systems. .
650		0	_aImage processing _xDigital techniques. _9118
650		0	_aComputer vision. _92455
650		0	_aArtificial intelligence.
650		0	_aBioinformatics. _91895
650	1	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
650	2	4	_aArtificial Intelligence.
650	2	4	_aComputational and Systems Biology.
655		7	_aElectronic books _2local _92032
700	1		_aEngelhardt, Sandy, _eeditor.
776	0	8	_z3-030-88209-8
830		0	_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v13003
856	4	0	_3Springerlink _uhttps://link.springer.com/book/10.1007/978-3-030-88210-5 _zOnline access link to the resource
942			_2NLM _cEBK
999			_c200467686 _d85898