Code for automatic labeling of special diagnostic mammography views from images and DICOM headers

Reference: DOI: 10.1007/s10278-018-0154-z

DICOM

Extract selected fields from DICOM headers

dicom_header_extraction/extract_dicom_headers_w_generator_150K.py

Normalize / expand data

dicom_header_extraction/normalize_selected_dcm_headers.py

Machine learning on DICOM headers

caret_on_headers.R       # most methods 
caret_on_headers_nona.R  # GLMNET

Image pipeline

Preprocessing

originally DICOMs were converted to 299x299 PNGs using convert_dicom_list_to_png.sh script

Weight files are available here

General image model

• scripts and config files: image_classifiers/e5ce2d69b035975cb5336cec0da9a32a

• weight file: model-272-general-e5ce2d69b035975cb5336cec0da9a32a.hdf5

Wire localization model

• scripts and config files: image_classifiers/e8e71fc090141d7c6fb334359152d295

• weight file: model-134-wire-e8e71fc090141d7c6fb334359152d295.hdf5

Visualization of performance metrics

Scripts used to generate Fig. 1

combine_predictions_hdr_and_img.ipynb
visualize_predictions_hdr_and_img.ipynb

Significance tests

Scripts used to generate Supplementary Figures S1 & S2

calc_auroc_confidence_intervals.R
plot_auroc_difference_pvalue.ipynb