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52 lines
1.4 KiB
Markdown
52 lines
1.4 KiB
Markdown
# Code for automatic labeling of special diagnostic mammography views from images and DICOM headers
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Reference: DOI: [10.1007/s10278-018-0154-z](https://www.ncbi.nlm.nih.gov/pubmed/30465142)
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## DICOM
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### Extract selected fields from DICOM headers
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dicom_header_extraction/extract_dicom_headers_w_generator_150K.py
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### Normalize / expand data
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dicom_header_extraction/normalize_selected_dcm_headers.py
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### Machine learning on DICOM headers
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caret_on_headers.R # most methods
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caret_on_headers_nona.R # GLMNET
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## Image pipeline
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### Preprocessing
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originally DICOMs were converted to 299x299 PNGs using `convert_dicom_list_to_png.sh` script
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### Weight files are available [here](https://datadryad.org/stash/dataset/doi:10.7272/Q6XK8CQ9)
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### General image model
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- scripts and config files: `image_classifiers/e5ce2d69b035975cb5336cec0da9a32a`
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- weight file: `model-272-general-e5ce2d69b035975cb5336cec0da9a32a.hdf5`
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### Wire localization model
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- scripts and config files: `image_classifiers/e8e71fc090141d7c6fb334359152d295`
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- weight file: `model-134-wire-e8e71fc090141d7c6fb334359152d295.hdf5`
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## Visualization of performance metrics
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Scripts used to generate Fig. 1
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combine_predictions_hdr_and_img.ipynb
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visualize_predictions_hdr_and_img.ipynb
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## Significance tests
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Scripts used to generate Supplementary Figures S1 & S2
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calc_auroc_confidence_intervals.R
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plot_auroc_difference_pvalue.ipynb
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