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Title:           Domain generalization in deep learning for contrast-enhanced imaging
Subject:         Computers in Biology and Medicine, 149 (2022) 106052. doi:10.1016/j.compbiomed.2022.106052
Keywords:        Deep learning,Contrast-enhanced imaging,Domain generalization,Cardiac image segmentation,Data augmentation,Transfer learning
Author:          Carla Sendra-Balcells
Creator:         Elsevier
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     Item: 
      Title: Domain generalization in deep learning for contrast-enhanced imaging
      Children: 
       Item: 
        Title: Introduction
        Children: 
         Item: 
          Title: Problem and motivation
         Item: 
          Title: Goals and contributions
       Item: 
        Title: Methods
        Children: 
         Item: 
          Title: Datasets
          Children: 
           Item: 
            Title: EMIDEC dataset: University Hospital Dijon, France
           Item: 
            Title: MSCMR dataset: Shanghai Renji Hospital, China
           Item: 
            Title: VH dataset: Vall d'Hebron Hospital, Spain
           Item: 
            Title: STPAU dataset: Sant Pau Hospital, Spain
         Item: 
          Title: Single-center model with data augmentation
         Item: 
          Title: Image harmonization at testing
         Item: 
          Title: Transfer learning from the original to the new clinical site
         Item: 
          Title: Multi-center model
         Item: 
          Title: Baseline workflow
          Children: 
           Item: 
            Title: Pre-processing
           Item: 
            Title: Post-processing
           Item: 
            Title: Network architecture
           Item: 
            Title: Implementation details
           Item: 
            Title: Performance evaluation
       Item: 
        Title: Results
        Children: 
         Item: 
          Title: Experiment 1: Effect of data augmentation
         Item: 
          Title: Experiment 2: Effect of image harmonization
         Item: 
          Title: Experiment 3: Effect of transfer learning
         Item: 
          Title: Experiment 4: Comparison to a multi-center scenario
         Item: 
          Title: Qualitative analysis
       Item: 
        Title: Discussion
       Item: 
        Title: Conclusions
       Item: 
        Title: CRediT authorship contribution statement
       Item: 
        Title: Declaration of competing interest
       Item: 
        Title: Data availability
       Item: 
        Title: References
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    Author: Carla Sendra-Balcells
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   Type: CRC32
  Checksum: 81b8425185e326685722f3bd5c12cc6f
   Type: MD5
  Checksum: 5eaee0bed50aa46daf1f7b312f85f810cc6c5d50
   Type: SHA-1
  Checksum: 860aa39d3acfa17bc383cd448c61674af68a191daedbffbdbac5fbe08f169f04
   Type: SHA-256