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""" |
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Created on Wed Apr 17 19:47:32 2024 |
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@author: yanwe |
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""" |
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import pandas as pd |
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import shutil |
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import os |
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test = pd.read_csv('/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/MIMIC_CXR/60k/meta/test.csv', nrows=500) |
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test['path_preproc'] = None |
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for n in range(500): |
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test.loc[n,'path_preproc'] = f'{n}.jpg' |
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source_file = '/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/MIMIC_CXR/60k/data' |
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target_directory = '/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/cf_app/data/mimic_subset' |
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for n in range(500): |
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file_name = os.path.join(source_file, test.loc[n,'dicom_id'] + '.jpg') |
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new_name = os.path.join(target_directory, test.loc[n,'path_preproc']) |
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shutil.copy(file_name, new_name) |
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def label_to_name(data): |
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if data['disease_label'] == 0: |
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return "No Finding" |
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elif data['disease_label'] == 1: |
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return "Pleural Effusion" |
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elif data['disease_label'] == 2: |
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return "Pneumonia" |
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else: |
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return "No Finding" |
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def label_to_sex(data): |
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if data['sex_label'] == 0: |
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return "Female" |
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elif data['sex_label'] == 1: |
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return "Male" |
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else: |
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return "Female" |
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def label_to_race(data): |
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if data['race_label'] == 0: |
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return "White" |
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elif data['race_label'] == 1: |
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return "Black" |
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elif data['race_label'] == 2: |
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return "Asian" |
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test['disease'] = test.apply(label_to_name, axis=1) |
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test['sex'] = test.apply(label_to_sex, axis=1) |
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test['race'] = test.apply(label_to_race, axis=1) |
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test.to_csv('/mnt/de1dcd1c-9be8-42ed-aa06-bb73570121ac/cf_app/data/mimic_subset/mimic.sample.test.csv') |
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