Module radiology_ai.preprocessing

Preprocessing functions, Transforms and Pytorch Dataset allowing to treat DICOM files and improve CT scan interpretations for Deep Neural Networks

Preprocessing

Raw images could be used by a deep learning model, however we can benefit from preprocessing. On this project the main three preprocess transformation are:

• KneeLocalizer: Center image only on region of interest (ROI) which normalize dataset.
• BackgroundPreprocess: Cleaning background and noisy parts which helps avoid model memorization due to characteristic background/noise.
• CLAHE Transform: Grayscale/Color histogram normalization which helps CNN model training.

See below some examples these transformations:

""" 
Preprocessing functions, `Transforms` and `Pytorch Dataset` allowing to treat DICOM files and improve CT scan interpretations for Deep Neural Networks

.. include:: README.md
"""

Sub-modules

radiology_ai.preprocessing.dicom

Preprocessings used for DICOM treatments

radiology_ai.preprocessing.misc

Miscellaneous preprocessings

radiology_ai.preprocessing.transforms

Fastai Transformations to be used on training