000916109 000__ 05380cam\a2200589Ii\4500
000916109 001__ 916109
000916109 005__ 20230306150524.0
000916109 006__ m\\\\\o\\d\\\\\\\\
000916109 007__ cr\cn\nnnunnun
000916109 008__ 191030s2019\\\\sz\a\\\\o\\\\\101\0\eng\d
000916109 019__ $$a1125991316$$a1126610496
000916109 020__ $$a9783030338435$$q(electronic book)
000916109 020__ $$a3030338436$$q(electronic book)
000916109 020__ $$z9783030338428
000916109 0247_ $$a10.1007/978-3-030-33843-5$$2doi
000916109 0247_ $$a10.1007/978-3-030-33
000916109 035__ $$aSP(OCoLC)on1125341728
000916109 035__ $$aSP(OCoLC)1125341728$$z(OCoLC)1125991316$$z(OCoLC)1126610496
000916109 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dLQU$$dUKMGB
000916109 049__ $$aISEA
000916109 050_4 $$aQ325.5
000916109 08204 $$a006.3/1$$223
000916109 1112_ $$aMLMIR (Workshop)$$n(2nd :$$d2019 :$$cShenzhen Shi, China)
000916109 24510 $$aMachine learning for medical image reconstruction :$$bsecond International Workshop, MLMIR 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings /$$cFlorian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye (eds.).
000916109 2463_ $$aMLMIR 2019
000916109 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000916109 300__ $$a1 online resource (xi, 266 pages) :$$billustrations.
000916109 336__ $$atext$$btxt$$2rdacontent
000916109 337__ $$acomputer$$bc$$2rdamedia
000916109 338__ $$aonline resource$$bcr$$2rdacarrier
000916109 4901_ $$aLecture notes in computer science ;$$v11905
000916109 4901_ $$aLNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
000916109 500__ $$aInternational conference proceedings.
000916109 500__ $$aIncludes author index.
000916109 5050_ $$aDeep Learning for Magnetic Resonance Imaging -- Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction- Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging -- Fast Dynamic Perfusion and Angiography Reconstruction using an end-to-end 3D Convolutional Neural Network -- APIR-Net: Autocalibrated Parallel Imaging Reconstruction using a Neural Network -- Accelerated MRI Reconstruction with Dual-domain Generative Adversarial Network -- Deep Learning for Low-Field to High-Field MR: Image Quality Transfer with Probabilistic Decimation Simulator -- Joint Multi-Anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions -- Modeling and Analysis Brain Development via Discriminative Dictionary Learning -- Deep Learning for Computed Tomography -- Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval -- Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior -- Measuring CT Reconstruction Quality with Deep Convolutional Neural Networks -- Deep Learning based Metal Inpainting in the Projection Domain: Initial Results -- Deep Learning for General Image Reconstruction -- Flexible Conditional Image Generation of Missing Data with Learned Mental Maps -- Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation -- Stain Style Transfer using Transitive Adversarial Networks -- Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer -- Deep Learning based approach to quantification of PET tracer uptake in small tumors -- Task-GAN: Improving Generative Adversarial Network for Image Reconstruction -- Gamma Source Location Learning from Synthetic Multi-Pinhole Collimator Data -- Neural Denoising of Ultra-Low Dose Mammography -- Image Reconstruction in a Manifold of Image Patches: Application to Whole-fetus Ultrasound Imaging -- Image Super Resolution via B ilinear Pooling: Application to Confocal Endomicroscopy -- TPSDicyc: Improved Deformation Invariant Cross-domain Medical Image Synthesis -- PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction.
000916109 506__ $$aAccess limited to authorized users.
000916109 520__ $$aThis book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.
000916109 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 30, 2019).
000916109 650_0 $$aMachine learning$$vCongresses.
000916109 650_0 $$aArtificial intelligence$$xMedical applications$$vCongresses.
000916109 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
000916109 7001_ $$aKnoll, Florian,$$eeditor.
000916109 7001_ $$aMaier, Andreas,$$eeditor.
000916109 7001_ $$aRueckert, Daniel,$$eeditor.
000916109 7001_ $$aYe, Jong Chul,$$eeditor.
000916109 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(22nd :$$d2019 :$$cShenzhen Shi, China),$$jjointly held conference.
000916109 830_0 $$aLecture notes in computer science ;$$v11905.
000916109 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000916109 852__ $$bebk
000916109 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-33843-5$$zOnline Access$$91397441.1
000916109 909CO $$ooai:library.usi.edu:916109$$pGLOBAL_SET
000916109 980__ $$aEBOOK
000916109 980__ $$aBIB
000916109 982__ $$aEbook
000916109 983__ $$aOnline
000916109 994__ $$a92$$bISE