Noisy Label Learning

pymic_nll

pymic_nll is the command for using built-in NLL 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_nll train myconfig_nll.cfg
pymic_nll test  myconfig_nll.cfg

Tip

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

Note

Some NLL methods only use noise-robust loss functions without complex training process, and just combining the standard SegmentationAgent with such loss function works for training. pymic_run instead of pymic_nll should be used for these methods.

NLL Configurations

In the configuration file for pymic_nll, in addition to those used in standard fully supervised learning, there is a noisy_label_learning section that is specifically designed for NLL methods. In that section, users need to specify the nll_method and configurations related to the NLL method. For example, the correspoinding configuration for CoTeaching is:

[dataset]
...

[network]
...

[training]
...

[noisy_label_learning]
nll_method   = CoTeaching
co_teaching_select_ratio  = 0.8
rampup_start = 1000
rampup_end   = 8000

[testing]
...

Note

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

Built-in NLL Methods

Some NLL methods only use noise-robust loss functions. They are used with pymic_run for training. Just set loss_type to one of them in the configuration file, similarly to the fully supervised learning.

GCELoss: (NeurIPS 2018) Generalized cross entropy loss.
MAELoss: (AAAI 2017) Mean Absolute Error loss.
NRDiceLoss: (TMI 2020) Noise-robust Dice loss.

The other NLL methods are implemented in child classes of pymic.net_run_nll.nll_abstract.NLLSegAgent, and they are:

CLSLSR: (MICCAI 2020) Confident learning with spatial label smoothing regularization.
CoTeaching: (NeurIPS 2018) Co-teaching between two networks for learning from noisy labels.
TriNet: (MICCAI 2020) Tri-network combined with sample selection.
DAST: (JBHI 2022) Divergence-aware selective training.

Customized NLL Methods

PyMIC alo supports customizing NLL methods by inheriting the NLLSegAgent 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_nll.nll_abstract import NLLSegAgent

class MyNLLMethod(NLLSegAgent):
  def __init__(self, config, stage = 'train'):
      super(MyNLLMethod, self).__init__(config, stage)
      ...

  def training(self):
      ...

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

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

In addition, if you want to design a new noise-robust loss fucntion, just follow Fully Supervised Learning to impelement and use the customized loss.