mammopy 0.0.1

A unified solution for mammogram image analysis and interpretation

MamToolKit

A Comprehensive Deep Learning Library for Mammogram Assessment

[Documentation] | [Paper] | [Notebook examples] | [Web applications]

Welcome to MamToolKit Repository! MamToolKit is a python-based library designed to facilitate the creation of mammogram image analysis pipelines . The library includes plug-and-play modules to perform:

• Standard mammogram image pre-processing (e.g., normalization, bounding box cropping, and DICOM to jpeg conversion)
• Mammogram assessment pipelines (e.g., breast area segmentation, dense tissue segmentation, and percentage density estimation)
• Modeling deep learning architectures for various downstream tasks (e.g., micro-calcification and mass detection)
• Feature attribution-based interpretability techniques (e.g., GradCAM, GradCAM++, and LRP)
• Visualization

All the functionalities are grouped under a user-friendly API.

If you encounter any issue or have questions regarding the library, feel free to open a GitHub issue. We'll do our best to address it.

Installation

PyPI installer (recommended)

pip install -U mamtoolkit

Development setup

• Clone the repo:

git clone https://github.com/uefcancer/mktoolkit/mamtoolkit.git && cd mamtoolkit

• Create a conda environment:

conda env create -f environment.yml

NOTE: To use GPUs, install GPU compatible Pytorch, Torchvision packages according to your OS, package manager, and CUDA.

• Activate it:

conda activate mamtoolkit

• Add MamToolKit to your python path:

export PYTHONPATH="<PATH>/mamtoolkit:$PYTHONPATH"

Using MamToolKit

The MamToolKit library provides a set of helpers grouped in different modules, namely preprocessing, assessment, interpretability, and visualization.

For instance, with MamToolKit library, we can load percentage mammogram density model (described in the [Paper] ) and predict the non-dense area, dense tissue area, and estimate percentage density:

import mamtoolkit as mg

model = mg.load_model("base")
result = mg.percentage_density(model, image_path) #path to mammogram image
print(result)

The percentage_density() method is a part of assessment module, which pre-processes the input image and converts it into the appropriate format and dimension accepted by the model. Then the model provides the analysis in forms of a dictionary with keys: non_dense_area, dense_area, and density.

Notice: The non_dense_area and dense_area are calculated in cm^2

result['non_dense_area'] = 
result['dense_area'] = 
result['density']

License

The MamToolKit library is released under the MIT License. See [LICENSE] (https://github.com/openai/whisper/blob/main/LICENSE) for further details.

If you use this library, please consider citing:

@article{gudhe2022area,
  title={Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning},
  author={Gudhe, Naga Raju and Behravan, Hamid and Sudah, Mazen and Okuma, Hidemi and Vanninen, Ritva and Kosma, Veli-Matti and Mannermaa, Arto},
  journal={Scientific reports},
  volume={12},
  number={1},
  pages={12060},
  year={2022},
  publisher={Nature Publishing Group UK London}
}