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: Machine Learning in Medical Imaging
    sub title: 11th International Workshop, MLMI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings
  name:
    name part: Liu, Mingxia.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Yan, Pingkun.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Lian, Chunfeng.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Cao, Xiaohuan.
    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
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XV, 686 pages 327 illustrations, 230 illustrations in color.)
  abstract: This book constitutes the proceedings of the 11th International Workshop on Machine Learning in Medical Imaging, MLMI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 68 papers presented in this volume were carefully reviewed and selected from 101 submissions. They focus on major trends and challenges in the above-mentioned area, aiming to identify new-cutting-edge techniques and their uses in medical imaging. Topics dealt with are: deep learning, generative adversarial learning, ensemble learning, sparse learning, multi-task learning, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection and diagnosis, multi-modality fusion, image reconstruction, image retrieval, cellular image analysis, molecular imaging, digital pathology, et cetera.
  table of contents: Temporal-Adaptive Graph Convolutional Network for Automated Identification of Major Depressive Disorder with Resting-State fMRI -- Error Attention Interactive Segmentation of Medical Images through Matting and Fusion -- A Novel fMRI Representation Learning Framework with GAN -- Semi-supervised Segmentation with Self-Training Based on Quality Estimation and Refinement -- 3D Segmentation Networks for Excessive Numbers of Classes: Distinct Bone Segmentation in Upper Bodies -- Super Resolution of Arterial Spin Labeling MR Imaging Using Unsupervised Multi-Scale Generative Adversarial Network -- Self-Recursive Contextual Network for Unsupervised 3D Medical Image Registration -- Automated Tumor Proportion Scoring for Assessment of PD-L1 Expression Based on Multi-Stage Ensemble Strategy -- Uncertainty Quantification in Medical Image Segmentation with Normalizing Flows -- Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest -- A 3D+2D CNN Approach Incorporating Boundary Loss for Stroke Lesion Segmentation -- Linking Adolescent Brain MRI to Obesity via Deep Multi-cue Regression Network -- Robust Multiple Sclerosis Lesion Inpainting with Edge Prior -- Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation -- GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes -- Anatomy-Aware Cardiac Motion Estimation -- Division and Fusion: Rethink Convolutional Kernels for 3D Medical Image Segmentation -- LDGAN: Longitudinal-Diagnostic Generative Adversarial Network for Disease Progression Prediction with Missing Structural MRI -- Unsupervised MRI Homogenization: Application to Pediatric Anterior Visual Pathway Segmentation -- Boundary-aware Network for Kidney Tumor Segmentation -- O-Net: An Overall Convolutional Network for Segmentation Tasks -- Label-Driven Brain Deformable Registration Using Structural Similarity and Nonoverlap Constraints -- EczemaNet: Automating Detec…
  note: edited by Mingxia Liu, Pingkun Yan, Chunfeng Lian, Xiaohuan Cao.
  subject:
    topic: Optical data processing
  subject:
    topic: Artificial intelligence
  subject:
    topic: Pattern recognition
  subject:
    topic: Application software
  subject:
    topic: Image Processing and Computer Vision
  subject:
    topic: Artificial Intelligence
  subject:
    topic: Pattern Recognition
  subject:
    topic: Computer Applications
  classification: TA1630-1650
  classification: 006.6
  classification: 006.37
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12436
  identifier: 9783030598617
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 201002
    record change date: 20230330185450.9
    record identifier: in00004427617