Updated the 'example' and 'full_data' configuration.

ColonCancerCTDatasetScript.py

@@ -8,46 +8,29 @@ import re
 import s3fs
 import random
 
-manifest_url = "https://drive.google.com/uc?id=1JBkQTXeieyN9_6BGdTF_DDlFFyZrGyU6"
-manifest_file = gdown.download(manifest_url, 'manifest_file.s5cmd', quiet=False)
+example_manifest_url = "https://drive.google.com/uc?id=1JBkQTXeieyN9_6BGdTF_DDlFFyZrGyU6"
+example_manifest_file = gdown.download(example_manifest_url, 'manifest_file.s5cmd', quiet = False)
+full_manifest_url = "https://drive.google.com/uc?id=1KP6qxcQoPF4MJdEPNwW7J6BlL_sUJ17j"
+full_manifest_file = gdown.download(full_manifest_url, 'full_manifest_file.s5cmd', quiet = False)
 fs = s3fs.S3FileSystem(anon=True)
 
-_DESCRIPTION = """
-This dataset, curated from the comprehensive collection by the National Cancer Institute (NCI)
-and hosted on AWS, contains over 900,000 colon CT images, along with the corresponding patients'
-information. It is designed to help researcher in developing advanced machine learning models
-for in-depth studies in colon cancer.
-"""
+_DESCRIPTION = "This is the description"
 _HOMEPAGE = "https://imaging.datacommons.cancer.gov/"
 _LICENSE = "https://fairsharing.org/FAIRsharing.0b5a1d"
-_CITATION = """\
-@article{fedorov2021nci,
-  title={NCI imaging data commons},
-  author={Fedorov, Andrey and Longabaugh, William JR and Pot, David
-  and Clunie, David A and Pieper, Steve and Aerts, Hugo JWL and
-  Homeyer, Andr{\'e} and Lewis, Rob and Akbarzadeh, Afshin and
-  Bontempi, Dennis and others},
-  journal={Cancer research},
-  volume={81},
-  number={16},
-  pages={4188--4193},
-  year={2021},
-  publisher={AACR}
-}
-"""
+_CITATION = "National Cancer Institute Imaging Data Commons (IDC) Collections was accessed on DATE from https://registry.opendata.aws/nci-imaging-data-commons"
+
 
 class ColonCancerCTDataset(datasets.GeneratorBasedBuilder):
-  """This dataset script retrieves the dataset using a manifest file from the original dataset's
-  homepage. The file lists the S3 paths for each series of CT images and metadata, guiding the download
-  from AWS. After processing the original content, this dataset will contian the image of the colonography, 
-  image type, study date, series date, manufacturer details, study descriptions, series descriptions, 
-  and patient demographics including sex, age, and pregnancy status.
-  """
+  """TODO: Short description of my dataset."""
   VERSION = datasets.Version("1.1.0")
 
+  BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="example", version=VERSION, description="This is a subset of the full dataset for demonstration purposes"),
+        datasets.BuilderConfig(name="full_data", version=VERSION, description="This is the complete dataset"),
+    ]
+  DEFAULT_CONFIG_NAME = "example"
+
   def _info(self):
-    """Returns DatasetInfo."""
-    # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
     return datasets.DatasetInfo(
         description=_DESCRIPTION,
         features=datasets.Features(
@@ -67,24 +50,29 @@ class ColonCancerCTDataset(datasets.GeneratorBasedBuilder):
         homepage = _HOMEPAGE,
         license = _LICENSE,
         citation = _CITATION
-        )
+        
+    )
 
   def _split_generators(self, dl_manager):
-    """Returns a list of SplitGenerators."""
-    # This method is tasked with extracting the S3 paths of the data and defining the splits 
-    # by shuffling and randomly partitioning the paths in the manifest file.
+    """Returns SplitGenerators."""
+    # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the
     s3_series_paths = []
     s3_individual_paths = []
+    if self.config.name == 'example':
+        manifest_file = example_manifest_file
+    else:
+        manifest_file = full_manifest_file
+    
     with open(manifest_file, 'r') as file:
-      for line in file:
-          match = re.search(r'cp (s3://[\S]+) .', line)
-          if match:
-              s3_series_paths.append(match.group(1)[:-2]) # Deleting the '/*' in directories
+        for line in file:
+            match = re.search(r'cp (s3://[\S]+) .', line)
+            if match:
+                s3_series_paths.append(match.group(1)[:-2]) # Deleting the '/*' in directories
     for series in s3_series_paths:
         for content in fs.ls(series):
-          s3_individual_paths.append(fs.info(content)['Key']) # Retrieve the individual DICOM file's S3 path
+            s3_individual_paths.append(fs.info(content)['Key'])
 
-    random.shuffle(s3_individual_paths) # Randomly shuffles the paths for partitioning
+    random.shuffle(s3_individual_paths)
 
     # Define the split sizes
     train_size = int(0.7 * len(s3_individual_paths))
@@ -120,13 +108,12 @@ class ColonCancerCTDataset(datasets.GeneratorBasedBuilder):
 
   def _generate_examples(self, paths, split):
     """Yields examples."""
-    # This method will yield examples, i.e. rows in the dataset.
+    # TODO: This method will yield examples, i.e. rows in the dataset.
     for path in paths:
       key = path
       with fs.open(path, 'rb') as f:
         dicom_data = pydicom.dcmread(f)
         pixel_array = dicom_data.pixel_array
-        # Converting pixel array into PNG image
         # Adjust for MONOCHROME1 to invert the grayscale values
         if dicom_data.PhotometricInterpretation == "MONOCHROME1":
           pixel_array = np.max(pixel_array) - pixel_array