Code Examples

Install

Install medigan library from pypi (or github).

pip install medigan

Import medigan and initialize Generators

from medigan import Generators
generators = Generators()

Generate Images

Generate 10 samples using one (model 1 is 00001_DCGAN_MMG_CALC_ROI) of the medigan models from the config.

install_dependencies signals to medigan that the user wishes to automatically install all the python dependencies (e.g. numpy. torch, etc) required to run this model (i.e. to the user's active python environment).

generators.generate(model_id=1, num_samples=10, install_dependencies=True)

Get the model's generate method and run it to generate 3 samples

# model 1 is "00001_DCGAN_MMG_CALC_ROI"
gen_function = generators.get_generate_function(model_id=1, num_samples=3)
gen_function()

Get the model's synthetic data as torch dataloader with 3 samples

# model 4 is "00004_PIX2PIX_MMG_MASSES_W_MASKS"
dataloader = generators.get_as_torch_dataloader(model_id=4, num_samples=3)

Visualize Generative Model

Displays an interactive visual interface for exploration of applicable models.

# model 10 is "00010_FASTGAN_POLYP_PATCHES_W_MASKS"
generators.visualize(10)

Search for Generative Models

Find all models that contain a specific key-value pair in their model config.

key = "modality"
value = "Full-Field Mammography"
found_models = generators.get_models_by_key_value_pair(key1=key, value1=value, is_case_sensitive=False)
print(found_models)

Create a list of search terms and find the models that have these terms in their config.

values_list = ['dcgan', 'Mammography', 'inbreast']
models = generators.find_matching_models_by_values(values=values_list, target_values_operator='AND', are_keys_also_matched=True, is_case_sensitive=False)
print(f'Found models: {models}')

Create a list of search terms, find a model and generate

values_list = ['dcgan', 'mMg', 'ClF', 'modalities', 'inbreast']
generators.find_model_and_generate(values=values_list, target_values_operator='AND', are_keys_also_matched=True, is_case_sensitive=False, num_samples=5)

Rank Generative Models

Rank the models by a performance metric and return ranked list of models

ranked_models = generators.rank_models_by_performance(metric="SSIM", order="asc")
print(ranked_models)

Find the models, then rank them by a performance metric and return ranked list of models

ranked_models = generators.find_models_and_rank(values=values_list, target_values_operator='AND', are_keys_also_matched=True, is_case_sensitive=False, metric="SSIM", order="asc")
print(ranked_models)

Find the models, then rank them, and then generate samples with the best ranked model.

generators.find_models_rank_and_generate(values=values_list, target_values_operator='AND', are_keys_also_matched=True, is_case_sensitive=False, metric="SSIM", order="asc", num_samples=5)