Weakly-Supervised Learning

pymic_wsl

pymic_wsl is the command for using built-in weakly-supervised methods for training. Similarly to pymic_run, it should be followed by two parameters, specifying the stage and configuration file, respectively. The training and testing commands are:

pymic_wsl train myconfig_wsl.cfg
pymic_wsl test  myconfig_wsl.cfg

Tip

If the WSL method only involves one network, either pymic_wsl or pymic_run can be used for inference. Their difference only exists in the training stage.

Note

Currently, the weakly supervised methods supported by PyMIC are only for learning from partial annotations, such scribble-based annotation. Learning from image-level or point annotations may involve several training stages and will be considered in the future.

WSL Configurations

In the configuration file for pymic_wsl, in addition to those used in fully supervised learning, there are some items specificalized for weakly-supervised learning.

First, in the train_transform list, a special transform named PartialLabelToProbability should be used to transform patial labels into a one-hot probability map and a weighting map of pixels (i.e., the weight of a pixel is 1 if labeled and 0 otherwise). The patial cross entropy loss on labeled pixels is actually implemented by a weighted cross entropy loss. The loss setting is loss_type = CrossEntropyLoss.

Second, there is a weakly_supervised_learning section that is specifically designed for WSL methods. In that section, users need to specify the wsl_method and configurations related to the WSL method. For example, the correspoinding configuration for GatedCRF is:

[dataset]
...
root_dir  = ../../PyMIC_data/ACDC/preprocess
train_csv = config/data/image_train.csv
valid_csv = config/data/image_valid.csv
test_csv  = config/data/image_test.csv

train_batch_size = 4

# data transforms
train_transform = [Pad, RandomCrop, RandomFlip, NormalizeWithMeanStd, PartialLabelToProbability]
valid_transform = [NormalizeWithMeanStd, Pad, LabelToProbability]
test_transform  = [NormalizeWithMeanStd, Pad]
...

[network]
...

[training]
...
loss_type     = CrossEntropyLoss
...

[weakly_supervised_learning]
wsl_method     = GatedCRF
regularize_w   = 0.1
rampup_start   = 2000
rampup_end     = 15000
GatedCRFLoss_W0     = 1.0
GatedCRFLoss_XY0    = 5
GatedCRFLoss_rgb    = 0.1
GatedCRFLoss_W1     = 1.0
GatedCRFLoss_XY1    = 3
GatedCRFLoss_Radius = 5

[testing]
...

Note

The configuration items vary with different WSL methods. Please refer to the API of each built-in WSL method for details of the correspoinding configuration.

Built-in WSL Methods

pymic.net_run_wsl.wsl_abstract.WSLSegAgent is the abstract class used for weakly-supervised learning. The built-in WSL methods are child classes of WSLSegAgent. The available WSL methods implemnted in PyMIC are listed in pymic.net_run_wsl.wsl_main.WSLMethodDict, and they are:

• EntropyMinimization: (NeurIPS 2005) Using entorpy minimization to regularize unannotated pixels.

• GatedCRF: (arXiv 2019) Use gated CRF to regularize unannotated pixels.

• TotalVariation: (arXiv 2022) Use Total Variation to regularize unannotated pixels.

• MumfordShah: (TIP 2020) Use Mumford Shah loss to regularize unannotated pixels.

• USTM: (PR 2022) Adapt USTM with transform-consistency regularization.

• DMPLS: (MICCAI 2022) Dynamically mixed pseudo label supervision

Customized WSL Methods

PyMIC alo supports customizing WSL methods by inheriting the WSLSegAgent class. You may only need to rewrite the training() method and reuse most part of the existing pipeline, such as data loading, validation and inference methods. For example:

from pymic.net_run_wsl.wsl_abstract import WSLSegAgent

class MyWSLMethod(WSLSegAgent):
  def __init__(self, config, stage = 'train'):
      super(MyWSLMethod, self).__init__(config, stage)
      ...

  def training(self):
      ...

agent = MyWSLMethod(config, stage)
agent.run()

You may need to check the source code of built-in WSL methods to be more familar with how to implement your own WSL method.