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0.0.13

  • Deep supervision added
  • Documentation updated

0.0.12

  • Misc bugfixes
  • Automatic check-pointing of the model has been added
  • Extending the codebase has been simplified
  • New optimizers added
  • New metrics added
  • Affine augmentation can now be significantly fine-tuned
  • Update logic for penalty calculation
  • RGB-specific augmentation added
  • Cropping added

0.0.11

  • Misc bugfixes for segmentation and classification
  • DFU 2021 parameter file added
  • Added SDNet for supervised learning - https://doi.org/10.1016/j.media.2019.101535
  • Added option to re-orient all images to canonical
  • Preprocessing and augmentation made into separate submodules

0.0.10

  • Half-time epoch loss and metric output added for increased information
  • Gradient clipping added
  • Per-epoch details in validation output added
  • Different types of normalization layer options added
  • Hausdorff as a validation metric has been added
  • New option to save preprocessed data before the training starts

0.0.9

  • Refactoring the training and inference code
  • Added offline mechanism to generate padded images to improve training RAM requirements

0.0.8

  • Pre-split training/validation data can now be provided
  • Major code refactoring to make extensions easier
  • Added a way to ignore a label during validation dice calculation
  • Added more options for VGG
  • Tests can now be run on GPU
  • New scheduling options added

0.0.7

  • New modality switch added for rad/path
  • Class list can now be defined as a range
  • Added option to train and infer on fused labels
  • Rotation 90 and 180 augmentation added
  • Cropping zero planes added for preprocessing
  • Normalization options added
  • Added option to save generated masks on validation and (if applicable) testing data

0.0.6

  • Added PyVIPS support
  • SubjectID-based split added

0.0.5

  • 2D support added
  • Pre-processing module added
    • Added option to threshold or clip the input image
  • Code consolidation
  • Added generic DenseNet
  • Added option to switch between Uniform and Label samplers
  • Added histopathology input (patch-based extraction)

0.0.4

  • Added full image validation for generating loss and dice scores
  • Nested cross-validation added
    • Collect statistics and plot them
  • Weighted DICE computation for handling class imbalances in segmentation

0.0.3

  • Added detailed documentation
  • Added MSE from Torch
  • Added option to parameterize model properties
    • Final convolution layer (softmax/sigmoid/none)
  • Added option to resize input dataset
  • Added new regression architecture (VGG)
  • Version checking in config file

0.0.2

  • More scheduling options
  • Automatic mixed precision training is now enabled by default
  • Subject-based shuffle for training queue construction is now enabled by default
  • Single place to parse and pass around parameters to make training/inference API easier to handle
  • Configuration file mechanism switched to YAML

0.0.1 (2020/08/25)

  • First tag of GaNDLF
  • Initial feature list:
    • Supports multiple
      • Deep Learning model architectures
      • Channels/modalities
      • Prediction classes
    • Data augmentation
    • Built-in cross validation