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# Code for automatic labeling of special diagnostic mammography views from images and DICOM headers
Reference: DOI: [10.1007/s10278-018-0154-z](https://www.ncbi.nlm.nih.gov/pubmed/30465142)
## 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](https://datadryad.org/stash/dataset/doi:10.7272/Q6XK8CQ9)
### 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