ldctbench.data.LDCTMayo

LDCTMayo(mode, args)

Bases: Dataset

Dataset class for the LDCT dataset

Parameters:

• mode (str) –

  Which subset to use. Must be train, val, or test.

• args (Namespace) –

  Command line argumetns passed to the dataset class.

Attributes:

• seed (int) –

  Random seed to use

• path (str) –

  Root path to datafolder

• patchsize (int) –

  Patchsize to use for training.

• eval_patchsize (int) –

  Patchsize to use for validation.

• data_subset (float) –

  Subset of the data to use.

• data_norm (str) –

  Normalization of the data, must be meanstd or minmax.

• info (dict) –

  Dictionary of info.yml associated with the dataset.

• samples (list) –

  List of all image slices

• weights (list) –

  List of weights associated with each slice. Slices of each patient having n_slices are weighted with 1/n_slices.

Examples:

Create a training dataset

>>> from argparse import Namespace
>>> from ldctbench.data import LDCTMayo
>>> args = Namespace(seed=1332, path='/path/to/dataset/root', data_norm='meanstd', data_subset=1.0, patchsize=128)
>>> train_data = LDCTMayo('train', args)

Create a validation dataset

>>> from argparse import Namespace
>>> from ldctbench.data import LDCTMayo
>>> args = Namespace(seed=1332, path='/path/to/dataset/root', data_norm='meanstd', data_subset=1.0, eval_patchsize=128)
>>> train_data = LDCTMayo('val', args)

__getitem__(idx)

Given an index, get slice, perform random cropping, normalize and return

__len__()

Length of dataset

denormalize(X)

Denormalize samples with precomputed mean/std or min/max

Parameters:

• X (ndarray) –

  Array to denormalize

Returns:

• ndarray –

  Denormalized array

Raises:

• ValueError –

  If normalization method self.data_norm is neihter meanstd nor minmax

reset_seed()

Reset random seeds

to_torch(X) staticmethod

Convert input image to torch tensor and unsqueeze