rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Information Processing in Medical Imaging
    sub title: 26th International Conference, IPMI 2019, Hong Kong, China, June 2-7, 2019, Proceedings
  name:
    name part: Chung, Albert C. S.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Gee, James C.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Yushkevich, Paul A.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Bao, Siqi.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIX, 884 p. 517 illus., 331 illus. in color. online resource.
  abstract: This book constitutes the proceedings of the 26th International Conference on Information Processing in Medical Imaging, IPMI 2019, held at the Hong Kong University of Science and Technology, Hong Kong, China, in June 2019. The 69 full papers presented in this volume were carefully reviewed and selected from 229 submissions. They were organized in topical sections on deep learning and segmentation; classification and inference; reconstruction; disease modeling; shape, registration; learning motion; functional imaging; and white matter imaging. The book also includes a number of post papers. .
  table of contents: Segmentation -- A Bayesian Neural Net to Segment Images with Uncertainty Estimates and Good Calibration -- Explicit Topological Priors for Deep-Learning Based Image Segmentation Using Persistent Homology -- Semi-Supervised and Task-Driven Data Augmentation -- Classification and Inference -- Analyzing Brain Morphology on the Bag-of-Features Manifold -- Modeling and Inference of Spatio-Temporal Protein Dynamics Across Brain Networks -- Deep Learning -- InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction -- Adaptive Graph Convolution Pooling for Brain Surface Analysis -- On Training Deep 3D CNN Models with Dependent Samples in Neuroimaging -- A Deep Neural Network for Manifold-Valued Data with Applications to Neuroimaging -- Improved Disease Classification in Chest X-rays with Transferred Features from Report Generation -- Reconstruction -- Limited Angle Tomography Reconstruction: Synthetic Reconstruction via Unsupervised Sinogram Adaptation -- Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences -- Disease Modeling -- Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia -- Shape -- Minimizing Non-Holonomicity: Finding Sheets in Fibrous Structures -- Learning Low-Dimensional Representations of Shape Data Sets with Diffeomorphic Autoencoders -- Diffeomorphic Medial Modeling -- Controlling Meshes via Curvature: Spin Transformations for Pose-Invariant Shape Processing -- Registration -- Local Optimal Transport for Functional Brain Template Estimation -- Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations -- Learning Motion -- Real-Time 2D-3D Deformable Registration with Deep Learning and Application to Lung Radiotherapy Targeting -- Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces -- Functional Imaging -- Integrating Convolutional Neural Networks and Probab…
  note: edited by Albert C. S. Chung, James C. Gee, Paul A. Yushkevich, Siqi Bao.
  subject:
    topic: Optical data processing
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer science-Mathematics
  subject:
    topic: Health informatics
  subject:
    topic: Computers
  subject:
    topic: Operating systems (Computers)
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Mathematics of Computing
  subject:
    topic: Health Informatics
  subject:
    topic: Models and Principles
  subject:
    topic: Operating Systems
  classification: TA1630-1650
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11492
  identifier: 9783030203511
  identifier: https://doi.org/10.1007/978-3-030-20351-1
  record info:
    record creation date: 190521
    record change date: 20191025042359.0
    record identifier: 978-3-030-20351-1