001437586 000__ 08967cam\a2200601\i\4500
001437586 001__ 1437586
001437586 003__ OCoLC
001437586 005__ 20230309004156.0
001437586 006__ m\\\\\o\\d\\\\\\\\
001437586 007__ cr\cn\nnnunnun
001437586 008__ 210625s2021\\\\sz\a\\\\o\\\\\101\0\eng\d
001437586 020__ $$a9783030781910$$q(electronic bk.)
001437586 020__ $$a3030781917$$q(electronic bk.)
001437586 020__ $$z9783030781903$$q(print)
001437586 0247_ $$a10.1007/978-3-030-78191-0$$2doi
001437586 035__ $$aSP(OCoLC)1257551919
001437586 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dOCLCO$$dOCLCF$$dOCLCO$$dEBLCP$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ
001437586 049__ $$aISEA
001437586 050_4 $$aRC78.7.D53$$bI54 2021eb
001437586 08204 $$a006.6$$223
001437586 1112_ $$aInternational Conference on Information Processing in Medical Imaging$$n(27th :$$d2021 :$$cOnline)
001437586 24510 $$aInformation processing in medical imaging :$$b27th International Conference, IPMI 2021, Virtual event, June 28-June 30, 2021, Proceedings /$$cAasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen (eds.).
001437586 2463_ $$aIPMI 2021
001437586 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001437586 300__ $$a1 online resource (xix, 782 pages) :$$billustrations (some color)
001437586 336__ $$atext$$btxt$$2rdacontent
001437586 337__ $$acomputer$$bc$$2rdamedia
001437586 338__ $$aonline resource$$bcr$$2rdacarrier
001437586 4901_ $$aLecture notes in computer science ;$$v12729
001437586 4901_ $$aLNCS sublibrary, SL 6, Image processing, computer vision, pattern recognition, and graphics
001437586 500__ $$aIncludes author index.
001437586 5050_ $$aRegistration -- Hypermorph: Amortized Hyperparameter Learning for Image Registration -- Deep learning based geometric registration for medical images: How accurate can we get without visual features -- Diffeomorphic registration with density changes for the analysis of imbalanced shapes -- Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's Continuum -- Multiple-shooting adjoint method for whole-brain dynamic causal modeling -- Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models -- Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training -- Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images -- MR Slice Profile Estimation by Learning to Match Internal Patch Distributions -- Partial Matching in the Space of Varifolds -- Nested Grassmanns for Dimensionality Reduction with Applications to Shape Analysis -- Hierarchical Morphology-Guided Tooth Instance Segmentation from CBCT Images -- Cortical Morphometry Analysis based on Worst Transportation Theory -- Geodesic B-Score for Improved Assessment of Knee Osteoarthritis -- Cytoarchitecture Measurements in Brain Gray Matter using Likelihood-Free Inference -- Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping -- Knowledge Transfer for Few-shot Segmentation of Novel White Matter Tracts -- Discovering Spreading Pathways of Neuropathological Events in Alzheimer's Disease Using Harmonic Wavelets -- A Multi-Scale Spatial and Temporal Attention Network on Dynamic Connectivity to Localize The Eloquent Cortex in Brain Tumor Patients -- Learning Multi-resolution Graph Edge Embedding for Discovering Brain Network Dysfunction in Neurological Disorders -- Equivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data -- Geodesic Tubes for Uncertainty Quantification in Diffusion MRI -- Structural Connectome Atlas Construction in the Space of Riemannian Metrics -- A Higher Order Manifold-valued Convolutional Neural Network with Applications in Diffusion MRI Processing -- Representation Disentanglement for Multi-modal Brain MR Analysis -- Variational Knowledge Distillation for Disease Classification in Chest X-Rays -- Information-based Disentangled Representation Learning for Unsupervised MR Harmonization -- A 3D SegNet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation -- Unsupervised Learning of Local Discriminative Representation for Medical Images -- TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer -- Segmenting two-dimensional structures with strided tensor networks -- Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation -- Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline -- Feature Library: A Benchmark for Cervical Lesion Segmentation -- Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation.-EnMcGAN: Adversarial Ensemble Learning for 3D Complete Renal Structures Segmentation -- Segmentation with Multiple Acceptable Annotations: A Case Study of Myocardial Segmentation in Contrast Echocardiography -- A New Bidirectional Unsupervised Domain Adaptation Segmentation Framework -- 3D Nucleus Instance Segmentation for Whole-Brain Microscopy Images -- Teach me to segment with mixed-supervision: confident students become masters -- Sequential modelling -- Future Frame Prediction for Robot-assisted Surgery -- Velocity-To-Pressure (V2P) -- Net: Inferring Relative Pressures from Time-Varying 3D Fluid Flow Velocities -- Lighting Enhancement Aids Reconstruction of Colonoscopic Surfaces -- Mixture modeling for identifying subtypes in disease course mapping -- Learning transition times in event sequences: the temporal event-based model of disease progression -- Learning with few or low quality labels -- Knowledge Distillation with Adaptive Asymmetric Label Sharpening for Semi-supervised Fracture Detection in Chest X-rays -- Semi-Supervised Screening of COVID-19 from Positive and Unlabeled Data with Constraint Non-Negative Risk Estimator -- Deep MCEM for Weakly-Supervised Learning to Jointly Segment and Recognize Objects using Very Few Expert Segmentations -- Weakly Supervised Deep Learning for Aortic Valve Finite Element Mesh Generation from 3D CT Images -- Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition -- Multimodal Self-Supervised Learning for Medical Image Analysis -- Uncertainty Quantification and Generative Modelling -- Spatially Varying Label Smoothing: Capturing Uncertainty from Expert Annotations -- Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection -- A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations -- Is segmentation uncertainty useful? -- Principled Ultrasound Data Augmentation for Classification of Standard Planes -- Adversarial Regression Learning for Bone Age Estimation -- Learning image quality assessment by reinforcing task amenable data selection -- Collaborative Multi-Agent Reinforcement Learning for Landmark Localization Using Continuous Action Space.
001437586 506__ $$aAccess limited to authorized users.
001437586 520__ $$aThis book constitutes the proceedings of the 27th International Conference on Information Processing in Medical Imaging, IPMI 2021, which was held online during June 28-30, 2021. The conference was originally planned to take place in Bornholm, Denmark, but changed to a virtual format due to the COVID-19 pandemic. The 59 full papers presented in this volume were carefully reviewed and selected from 200 submissions. They were organized in topical sections as follows: registration; causal models and interpretability; generative modelling; shape; brain connectivity; representation learning; segmentation; sequential modelling; learning with few or low quality labels; uncertainty quantification and generative modelling; and deep learning.
001437586 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 25, 2021).
001437586 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
001437586 650_6 $$aImagerie pour le diagnostic$$xInformatique$$vCongrès.
001437586 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001437586 655_7 $$aConference papers and proceedings.$$2lcgft
001437586 655_7 $$aActes de congrès.$$2rvmgf
001437586 655_0 $$aElectronic books.
001437586 7001_ $$aFeragen, Aasa,$$eeditor$$1https://orcid.org/0000-0002-9945-981X
001437586 7001_ $$aSommer, Stefan,$$eeditor$$0(orcid)0000-0001-6784-0328$$1https://orcid.org/0000-0001-6784-0328
001437586 7001_ $$aSchnabel, Julia,$$eeditor$$0(orcid)0000-0001-6107-3009$$1https://orcid.org/0000-0001-6107-3009
001437586 7001_ $$aNielsen, Mads,$$eeditor$$0(orcid)0000-0003-1535-068X$$1https://orcid.org/0000-0003-1535-068X
001437586 830_0 $$aLecture notes in computer science ;$$v12729.
001437586 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001437586 852__ $$bebk
001437586 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-78191-0$$zOnline Access$$91397441.1
001437586 909CO $$ooai:library.usi.edu:1437586$$pGLOBAL_SET
001437586 980__ $$aBIB
001437586 980__ $$aEBOOK
001437586 982__ $$aEbook
001437586 983__ $$aOnline
001437586 994__ $$a92$$bISE