<?xml version="1.0" encoding="UTF-8"?>
<collection xmlns="http://www.loc.gov/MARC21/slim">
 <record>
  <leader>08712nam a22005295i 4500</leader>
  <controlfield tag="001">978-3-030-78191-0</controlfield>
  <controlfield tag="003">DE-He213</controlfield>
  <controlfield tag="005">20210625094701.0</controlfield>
  <controlfield tag="007">cr nn 008mamaa</controlfield>
  <controlfield tag="008">210620s2021    gw |    s    |||| 0|eng d</controlfield>
  <datafield tag="020" ind1=" " ind2=" ">
   <subfield code="a">9783030781910</subfield>
   <subfield code="9">978-3-030-78191-0</subfield>
  </datafield>
  <datafield tag="024" ind1="7" ind2=" ">
   <subfield code="a">10.1007/978-3-030-78191-0</subfield>
   <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="050" ind1=" " ind2="4">
   <subfield code="a">TA1630-1650</subfield>
  </datafield>
  <datafield tag="072" ind1=" " ind2="7">
   <subfield code="a">UYT</subfield>
   <subfield code="2">bicssc</subfield>
  </datafield>
  <datafield tag="072" ind1=" " ind2="7">
   <subfield code="a">COM012000</subfield>
   <subfield code="2">bisacsh</subfield>
  </datafield>
  <datafield tag="072" ind1=" " ind2="7">
   <subfield code="a">UYT</subfield>
   <subfield code="2">thema</subfield>
  </datafield>
  <datafield tag="072" ind1=" " ind2="7">
   <subfield code="a">UYQV</subfield>
   <subfield code="2">thema</subfield>
  </datafield>
  <datafield tag="082" ind1="0" ind2="4">
   <subfield code="a">006.6</subfield>
   <subfield code="2">23</subfield>
  </datafield>
  <datafield tag="082" ind1="0" ind2="4">
   <subfield code="a">006.37</subfield>
   <subfield code="2">23</subfield>
  </datafield>
  <datafield tag="245" ind1="1" ind2="0">
   <subfield code="a">Information Processing in Medical Imaging</subfield>
   <subfield code="h">[electronic resource] :</subfield>
   <subfield code="b">27th International Conference, IPMI 2021, Virtual Event, June 28–June 30, 2021, Proceedings /</subfield>
   <subfield code="c">edited by Aasa Feragen, Stefan Sommer, Julia Schnabel, Mads Nielsen.</subfield>
  </datafield>
  <datafield tag="250" ind1=" " ind2=" ">
   <subfield code="a">1st ed. 2021.</subfield>
  </datafield>
  <datafield tag="264" ind1=" " ind2="1">
   <subfield code="a">Cham :</subfield>
   <subfield code="b">Springer International Publishing :</subfield>
   <subfield code="b">Imprint: Springer,</subfield>
   <subfield code="c">2021.</subfield>
  </datafield>
  <datafield tag="300" ind1=" " ind2=" ">
   <subfield code="a">XIX, 782 p. 283 illus., 261 illus. in color.</subfield>
   <subfield code="b">online resource.</subfield>
  </datafield>
  <datafield tag="336" ind1=" " ind2=" ">
   <subfield code="a">text</subfield>
   <subfield code="b">txt</subfield>
   <subfield code="2">rdacontent</subfield>
  </datafield>
  <datafield tag="337" ind1=" " ind2=" ">
   <subfield code="a">computer</subfield>
   <subfield code="b">c</subfield>
   <subfield code="2">rdamedia</subfield>
  </datafield>
  <datafield tag="338" ind1=" " ind2=" ">
   <subfield code="a">online resource</subfield>
   <subfield code="b">cr</subfield>
   <subfield code="2">rdacarrier</subfield>
  </datafield>
  <datafield tag="347" ind1=" " ind2=" ">
   <subfield code="a">text file</subfield>
   <subfield code="b">PDF</subfield>
   <subfield code="2">rda</subfield>
  </datafield>
  <datafield tag="490" ind1="1" ind2=" ">
   <subfield code="a">Image Processing, Computer Vision, Pattern Recognition, and Graphics ;</subfield>
   <subfield code="v">12729</subfield>
  </datafield>
  <datafield tag="505" ind1="0" ind2=" ">
   <subfield code="a">Registration -- 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.</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
   <subfield code="a">This 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.</subfield>
  </datafield>
  <datafield tag="650" ind1=" " ind2="0">
   <subfield code="a">Optical data processing.</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="4">
   <subfield code="a">Image Processing and Computer Vision.</subfield>
   <subfield code="0">https://scigraph.springernature.com/ontologies/product-market-codes/I22021</subfield>
  </datafield>
  <datafield tag="700" ind1="1" ind2=" ">
   <subfield code="a">Feragen, Aasa.</subfield>
   <subfield code="e">editor.</subfield>
   <subfield code="0">(orcid)0000-0002-9945-981X</subfield>
   <subfield code="1">https://orcid.org/0000-0002-9945-981X</subfield>
   <subfield code="4">edt</subfield>
   <subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield>
  </datafield>
  <datafield tag="700" ind1="1" ind2=" ">
   <subfield code="a">Sommer, Stefan.</subfield>
   <subfield code="e">editor.</subfield>
   <subfield code="0">(orcid)0000-0001-6784-0328</subfield>
   <subfield code="1">https://orcid.org/0000-0001-6784-0328</subfield>
   <subfield code="4">edt</subfield>
   <subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield>
  </datafield>
  <datafield tag="700" ind1="1" ind2=" ">
   <subfield code="a">Schnabel, Julia.</subfield>
   <subfield code="e">editor.</subfield>
   <subfield code="0">(orcid)0000-0001-6107-3009</subfield>
   <subfield code="1">https://orcid.org/0000-0001-6107-3009</subfield>
   <subfield code="4">edt</subfield>
   <subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield>
  </datafield>
  <datafield tag="700" ind1="1" ind2=" ">
   <subfield code="a">Nielsen, Mads.</subfield>
   <subfield code="e">editor.</subfield>
   <subfield code="0">(orcid)0000-0003-1535-068X</subfield>
   <subfield code="1">https://orcid.org/0000-0003-1535-068X</subfield>
   <subfield code="4">edt</subfield>
   <subfield code="4">http://id.loc.gov/vocabulary/relators/edt</subfield>
  </datafield>
  <datafield tag="710" ind1="2" ind2=" ">
   <subfield code="a">SpringerLink (Online service)</subfield>
  </datafield>
  <datafield tag="773" ind1="0" ind2=" ">
   <subfield code="t">Springer Nature eBook</subfield>
  </datafield>
  <datafield tag="776" ind1="0" ind2="8">
   <subfield code="i">Printed edition:</subfield>
   <subfield code="z">9783030781903</subfield>
  </datafield>
  <datafield tag="776" ind1="0" ind2="8">
   <subfield code="i">Printed edition:</subfield>
   <subfield code="z">9783030781927</subfield>
  </datafield>
  <datafield tag="830" ind1=" " ind2="0">
   <subfield code="a">Image Processing, Computer Vision, Pattern Recognition, and Graphics ;</subfield>
   <subfield code="v">12729</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2="0">
   <subfield code="u">https://doi.org/10.1007/978-3-030-78191-0</subfield>
  </datafield>
  <datafield tag="912" ind1=" " ind2=" ">
   <subfield code="a">ZDB-2-SCS</subfield>
  </datafield>
  <datafield tag="912" ind1=" " ind2=" ">
   <subfield code="a">ZDB-2-SXCS</subfield>
  </datafield>
  <datafield tag="912" ind1=" " ind2=" ">
   <subfield code="a">ZDB-2-LNC</subfield>
  </datafield>
  <datafield tag="950" ind1=" " ind2=" ">
   <subfield code="a">Computer Science (SpringerNature-11645)</subfield>
  </datafield>
  <datafield tag="950" ind1=" " ind2=" ">
   <subfield code="a">Computer Science (R0) (SpringerNature-43710)</subfield>
  </datafield>
 </record>
</collection>
