dwiml_doc

Installing dwi_ml

  • Getting started: download and installation

Explanations for users (pre-trained)

  • Converting your data into a hdf5 file
    • The possibility of laziness
    • How to organize your data?
    • Preparing the config file
      • General group attributes in the config file:
      • Additional attributes for volume groups:
      • Additional attributes for streamlines groups:
    • Creating the hdf5
  • Using tractography models
    • Hdf5 preparation for our two tractography models
    • Tracking from a model
    • TractoTransformers (tt)
    • Learn2track (l2t)
  • Using denoising models

Explanations for explorers

  • Training tracking models: from start to finish
    • Overview of the process
    • Learn2track (l2t)
    • TractoTransformers (tt)
  • Training denoising models: from start to finish
  • Visualizing logs
    • Using Comet.ml
    • Using logs on your computer

Explanations for developers

  • Creating your own model
    • Inherit from MainModelAbstract
      • The MainModelAbstract class
      • Where to start?
    • You may inherit from many models!
      • Where to start?
      • General models
        • ModelWithPreviousDirections
        • ModelWithDirectionGetter
      • Models for usage with DWI inputs
        • ModelWithNeighborhood
        • ModelWithOneInput
    • The DirectionGetter Layer
      • General architecture
      • Regression models
      • Classification models
      • Models for indirect regression
        • Gaussian models
        • Fisher von mises models
  • The hdf5 structure
  • Training your model
    • Advantages of using our trainers
    • Overview of the process
    • Our choices of trainers
      • DWIMLTrainer
      • DWIMLTrainerOneInput
      • DWIMLTrainerOneInputWithGVPhase
    • Trainers: the code explained
  • Understanding our data management
    • The MultisubjectDataset
      • (Lazy)MultisubjectDataset
      • Multisubjectsubset
      • (Lazy)SubjectsDataList
      • (Lazy)SubjectData
      • MRIData
      • SFTData
    • Batch sampler
      • DWIMLBatchIDSampler
    • Batch loader
      • DWIMLStreamlinesBatchLoader
      • DWIMLBatchLoaderOneInput
  • Tracking with your model
    • Trackers
    • Differences with scilpy
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