Op mri

examples/fuse_examples/fuse_examples_utils.py

@@ -0,0 +1,15 @@
+import os
+def ask_user(yes_no_question):
+    res = ''
+    while res not in ['y', 'n']:
+        res = input(f'{yes_no_question}? [y/n]')
+    return res =='y'
+
+def get_fuse_examples_user_dir():
+    if 'USER_HOME_PATH' in os.environ:
+        fuse_examples_dir = os.path.join(os.environ['USER_HOME_PATH'],'fuse_examples')
+    else:
+        fuse_examples_dir = './fuse_examples'
+    if not os.path.exists(fuse_examples_dir):
+        os.mkdir(fuse_examples_dir)
+    return fuse_examples_dir

examples/fuse_examples/imaging/classification/duke_breast_cancer/__init__.py

@@ -0,0 +1,12 @@
+import os
+
+from fuse_examples.fuse_examples_utils import get_fuse_examples_user_dir
+
+
+def get_duke_user_dir():
+    return os.path.join(get_fuse_examples_user_dir(), 'duke')
+
+def get_duke_radiomics_user_dir():
+    return os.path.join(get_fuse_examples_user_dir(), ' duke_radiomics')
+
+

examples/fuse_examples/imaging/classification/duke_breast_cancer/debug/__init__.py

@@ -0,0 +1,61 @@
+import gzip
+import os
+import pickle
+
+import pandas as pd
+
+
+def save_object(obj, filename):
+    open_func = gzip.open if filename.endswith(".gz") else open
+    filename_tmp = filename+ ".del"
+    if os.path.exists(filename_tmp):
+        os.remove(filename_tmp)
+    with open_func(filename_tmp, 'wb') as output:
+        pickle.dump(obj, output, pickle.HIGHEST_PROTOCOL)
+
+    os.rename(filename_tmp, filename)
+    return filename
+
+
+def load_object(filename):
+    open_func = gzip.open if filename.endswith(".gz") else open
+
+    with open_func(filename, 'rb') as myinput:
+        try:
+            res = pickle.load(myinput)
+        except RuntimeError as e:
+            print("Failed to read", filename)
+            raise e
+    return res
+
+
+DUKE_PROCESSED_FILE_DIR = '/projects/msieve_dev3/usr/common/duke_processed_files'
+
+
+def get_duke_annotations_from_tal_df():
+    annotations_path = os.path.join(DUKE_PROCESSED_FILE_DIR, 'dataset_DUKE_folds_ver11102021TumorSize_seed1.pickle')
+    with open(annotations_path, 'rb') as infile:
+        fold_annotations_dict = pickle.load(infile)
+    annotations_df = pd.concat(
+        [fold_annotations_dict[f'data_fold{fold}'] for fold in range(len(fold_annotations_dict))])
+    return annotations_df
+
+
+def get_col_mapping():
+    return {'MRI Findings:Skin/Nipple Invovlement': 'Skin Invovlement',
+            'US features:Tumor Size (cm)': 'Tumor Size US',
+            'Mammography Characteristics:Tumor Size (cm)': 'Tumor Size MG',
+            'MRI Technical Information:FOV Computed (Field of View) in cm': 'Field of View',
+            'MRI Technical Information:Contrast Bolus Volume (mL)': 'Contrast Bolus Volume',
+            'Demographics:Race and Ethnicity': 'Race',
+            'MRI Technical Information:Manufacturer Model Name': 'Manufacturer',
+            'MRI Technical Information:Slice Thickness': 'Slice Thickness',
+            'MRI Findings:Multicentric/Multifocal': 'Multicentric',
+            'Mammography Characteristics:Breast Density': 'Breast Density MG',
+            'Tumor Characteristics:PR': 'PR',
+            'Tumor Characteristics:HER2': 'HER2',
+            'Tumor Characteristics:ER': 'ER',
+            'Near Complete Response:Overall Near-complete Response:  Stricter Definition': 'Near pCR Strict',
+            'Tumor Characteristics:Staging(Tumor Size)# [T]': 'Staging Tumor Size',
+            'Tumor Characteristics:Histologic type': 'Histologic type',
+            }