Semi-Supervised Learning

SSL Configurations

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

Users should provide values for the following items in dataset section of the configuration file:

supervise_type (string): The value should be “semi_sup”.
train_csv_unlab (string): the csv file for unlabeled dataset. Note that train_csv is only used for labeled dataset.
train_batch_size_unlab (int): the batch size for unlabeled dataset. Note that train_batch_size means the batch size for the labeled dataset.
train_transform_unlab (list): a list of transforms used for unlabeled data.

The following is an example of the dataset section for semi-supervised learning:

...

tensor_type    = float
task_type      = seg
supervise_type = semi_sup

root_dir  = ../../PyMIC_data/ACDC/preprocess/
train_csv = config/data/image_train_r10_lab.csv
train_csv_unlab = config/data/image_train_r10_unlab.csv
valid_csv = config/data/image_valid.csv
test_csv  = config/data/image_test.csv

train_batch_size = 4
train_batch_size_unlab = 4

# data transforms
train_transform = [Pad, RandomRotate, RandomCrop, RandomFlip, NormalizeWithMeanStd, GammaCorrection, GaussianNoise, LabelToProbability]
train_transform_unlab = [Pad, RandomRotate, RandomCrop, RandomFlip, NormalizeWithMeanStd, GammaCorrection, GaussianNoise]
valid_transform       = [NormalizeWithMeanStd, Pad, LabelToProbability]
test_transform        = [NormalizeWithMeanStd, Pad]
...

In addition, there is a semi_supervised_learning section that is specifically designed for SSL methods. In that section, users need to specify the method_name and configurations related to the SSL method. For example, the correspoinding configuration for CPS is:

...
[semi_supervised_learning]
method_name    = CPS
regularize_w   = 0.1
rampup_start   = 1000
rampup_end     = 20000
...

Note

The configuration items vary with different SSL methods. Please refer to the API of each built-in SSL method for details of the correspoinding configuration. See examples in PyMIC_examples/seg_ssl/.

Built-in SSL Methods

pymic.net_run.semi_sup.ssl_abstract.SSLSegAgent is the abstract class used for semi-supervised learning. The built-in SSL methods are child classes of SSLSegAgent. The available SSL methods implemnted in PyMIC are listed in pymic.net_run.semi_sup.SSLMethodDict, and they are:

EntropyMinimization: (NeurIPS 2005) Using entorpy minimization to regularize unannotated samples.
MeanTeacher: (NeurIPS 2017) Use self-ensembling mean teacher to supervise the student model on unannotated samples.
UAMT: (MICCAI 2019) Uncertainty aware mean teacher.
CCT: (CVPR 2020) Cross-consistency training.
CPS: (CVPR 2021) Cross-pseudo supervision.
URPC: (MIA 2022) Uncertainty rectified pyramid consistency.

Customized SSL Methods

PyMIC alo supports customizing SSL methods by inheriting the SSLSegAgent 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.semi_sup import SSLSegAgent

class MySSLMethod(SSLSegAgent):
  def __init__(self, config, stage = 'train'):
      super(MySSLMethod, self).__init__(config, stage)
      ...

  def training(self):
      ...

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

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