Upload msynth.py

msynth.py

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+# Copyright 2022 for msynth dataset
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+'''
+Custom dataset-builder for msynth dataset
+'''
+
+import os
+import datasets
+import glob
+import re
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = """\
+@article{sizikova2023knowledge,
+  title={Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses},
+  author={Sizikova, Elena and Saharkhiz, Niloufar and Sharma, Diksha and Lago, Miguel and Sahiner, Berkman and Delfino, Jana G. and Badano, Aldo},
+  journal={Advances in Neural Information Processing Systems},
+  volume={},
+  pages={16764--16778},
+  year={2023}
+"""
+
+
+_DESCRIPTION = """\
+M-SYNTH is a synthetic digital mammography (DM) dataset with four breast fibroglandular density distributions imaged using Monte Carlo x-ray simulations with the publicly available Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) toolkit.
+Curated by: Elena Sizikova, Niloufar Saharkhiz, Diksha Sharma, Miguel Lago, Berkman Sahiner, Jana Gut Delfino, Aldo Badano
+License: Creative Commons 1.0 Universal License (CC0)
+"""
+
+
+_HOMEPAGE = "link to the dataset description page (FDA/CDRH/OSEL/DIDSR/VICTRE_project)"
+
+_REPO = "https://huggingface.co/datasets/didsr/msynth/resolve/main/data"
+
+# satting parameters for the URLS
+_LESIONDENSITY = ["1.0","1.06", "1.1"]
+_DOSE = ["20%","40%","60%","80%","100%"]
+_DENSITY = ["fatty", "dense", "hetero","scattered"]
+_SIZE = ["5.0","7.0", "9.0"]
+_DETECTOR = 'SIM'
+
+_DOSETABLE = {
+    "dense": {
+        "20%": '1.73e09',
+        "40%": '3.47e09',
+        "60%": '5.20e09',
+        "80%": '6.94e09',
+        "100%": '8.67e09'
+    },
+    "hetero": {
+        "20%": '2.04e09',
+        "40%": '4.08e09',
+        "60%": '6.12e09',
+        "80%": '8.16e09',
+        "100%": '1.02e10'
+    },
+    "scattered": {
+        "20%": '4.08e09',
+        "40%": '8.16e09',
+        "60%": '1.22e10',
+        "80%": '1.63e10',
+        "100%": '2.04e10'
+    },
+    "fatty": {
+        "20%": '4.44e09',
+        "40%": '8.88e09',
+        "60%": '1.33e10',
+        "80%": '1.78e10',
+        "100%": '2.22e10'
+    }
+}
+# Links to download readme files
+_URLS = {
+    "meta_data": f"{_REPO}/metadata/bounds.zip",
+    "read_me": f"{_REPO}/README.md"
+}
+
+
+
+# Define the labels or classes in your dataset
+#_NAMES = ["raw", "mhd", "dicom", "loc"]  
+
+DATA_DIR = {"all_data": "SIM", "seg": "SIM", "info": "bounds"}
+
+class msynthConfig(datasets.BuilderConfig):
+    """msynth dataset"""
+    lesion_density = _LESIONDENSITY
+    dose = _DOSE
+    density = _DENSITY
+    size = _SIZE
+    def __init__(self, name, **kwargs):
+        super(msynthConfig, self).__init__(
+        version=datasets.Version("1.0.0"),
+        name=name,
+        description="msynth",
+        **kwargs,
+        )
+
+class msynth(datasets.GeneratorBasedBuilder):
+    """msynth dataset."""
+    
+    DEFAULT_WRITER_BATCH_SIZE = 256
+    BUILDER_CONFIGS = [
+        msynthConfig("device-data"),
+        msynthConfig("segmentation-mask"),
+        msynthConfig("metadata"),
+    ]
+    
+    def _info(self):
+        if self.config.name == "device-data":
+            # Define dataset features and keys
+            features = datasets.Features(
+                {       
+                    "Raw": datasets.Value("string"),
+                    "mhd": datasets.Value("string"),
+                    "loc": datasets.Value("string"),
+                    "dcm": datasets.Value("string"),                  
+                    "density": datasets.Value("string"),
+                    "mass_radius": datasets.Value("float32")
+                }
+            )
+        #keys = ("image", "metadata")
+        elif self.config.name == "segmentation-mask":
+            # Define features and keys
+            features = datasets.Features(
+                {       
+                    "Raw": datasets.Value("string"),
+                    "mhd": datasets.Value("string"),
+                    "loc": datasets.Value("string"),                 
+                    "density": datasets.Value("string"),
+                    "mass_radius": datasets.Value("float32")
+                }
+            )
+            
+        elif self.config.name == "metadata":
+            # Define features and keys
+            features = datasets.Features(
+                {       
+                    "fatty": datasets.Value("string"),
+                    "dense": datasets.Value("string"),
+                    "hetero": datasets.Value("string"),                 
+                    "scattered": datasets.Value("string")
+                }
+            )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+    
+    def _split_generators(
+        self, dl_manager: datasets.utils.download_manager.DownloadManager):
+        # Setting up the **config_kwargs parameters
+        if self.config.lesion_density == "all":
+            self.config.lesion_density = _LESIONDENSITY
+            
+        if self.config.dose == "all":
+            self.config.dose = _DOSE
+        
+        if self.config.density == "all":
+            self.config.density = _DENSITY
+   
+        if self.config.size == "all":
+            self.config.size = _SIZE
+        
+        
+        if self.config.name == "device-data":
+            file_name = []
+            for ld in self.config.lesion_density:
+                for ds in self.config.dose:
+                    for den in self.config.density:
+                        value = _DOSETABLE[den][ds]
+                        for sz in self.config.size:
+                            temp_name = []
+                            temp_name = (
+                                "device_data_VICTREPhantoms_spic_"
+                                + ld
+                                + "/"
+                                + value
+                                + "/"
+                                + den
+                                + "/2/"
+                                + sz
+                                + "/"
+                                + _DETECTOR
+                                + ".zip"
+                            )
+                            file_name.append(_REPO +"/"+ temp_name)
+            
+            # Downloading the data files
+            # data_dir = dl_manager.download_and_extract(file_name)
+            data_dir = []
+            for url in file_name:
+                try:
+                    temp_down_file = []
+                    # Attempt to download the file
+                    temp_down_file = dl_manager.download_and_extract(url)
+                    data_dir.append(temp_down_file)
+                
+                except Exception as e:
+                # If an exception occurs (e.g., file not found), log the error and add the URL to the failed_urls list
+                logger.error(f"Failed to download {url}: {e}")    
+
+            return [
+                datasets.SplitGenerator(
+                    name="device-data",
+                    gen_kwargs={
+                        "files": [data_dir_t for data_dir_t in data_dir],
+                        "name": "all_data",
+                    },
+                ),
+            ]
+        
+        elif self.config.name == "segmentation-mask":
+            seg_file_name = []
+            for den in self.config.density:
+                for sz in self.config.size:
+                    temp_name = []
+                    temp_name = (
+                        "segmentation_masks"
+                        + "/"
+                        + den
+                        + "/2/"
+                        + sz
+                        + "/"
+                        + _DETECTOR
+                        + ".zip"
+                    )
+                    seg_file_name.append(_REPO+ "/" + temp_name)
+
+            # Downloading the files
+            seg_dir = []
+            #seg_dir = dl_manager.download_and_extract(seg_file_name)
+            
+            for url in seg_file_name:
+                try:
+                    # Attempt to download the file
+                    temp_down_file = []
+                    temp_down_file = dl_manager.download_and_extract(url)
+                    seg_dir.append(temp_down_file)
+                
+                except Exception as e:
+                # If an exception occurs (e.g., file not found), log the error and add the URL to the failed_urls list
+                logger.error(f"Failed to download {url}: {e}")    
+            
+            return [
+                datasets.SplitGenerator(
+                    name="segmentation-mask",
+                    gen_kwargs={
+                        "files": [data_dir_t for data_dir_t in seg_dir],
+                        "name": "seg",
+                    },
+                ),
+            ]
+        
+        elif self.config.name == "metadata":
+            meta_dir = dl_manager.download_and_extract(_URLS['meta_data'])
+            return [
+                datasets.SplitGenerator(
+                    name="metadata",
+                    gen_kwargs={
+                        "files": meta_dir,
+                        "name": "info",
+                    },
+                ),
+            ]
+
+
+    def get_all_file_paths(self, root_directory):
+        file_paths = []  # List to store file paths
+
+        # Walk through the directory and its subdirectories using os.walk
+        for folder, _, files in os.walk(root_directory):
+            for file in files:
+                if file.endswith('.raw'):
+                # Get the full path of the file
+                    file_path = os.path.join(folder, file)
+                    file_paths.append(file_path)
+        return file_paths
+    
+    def get_support_file_path(self, raw_file_path, ext):        
+        folder_path = os.path.dirname(raw_file_path)
+        # Use os.path.basename() to extract the filename
+        raw_file_name = os.path.basename(raw_file_path)
+        # Use os.path.splitext() to split the filename into root and extension
+        root, extension = os.path.splitext(raw_file_name)
+        if ext == "dcm":
+            supp_file_name = f"000.{ext}"
+            file_path = os.path.join(folder_path,"DICOM_dm",supp_file_name)
+        else:
+            supp_file_name = f"{root}.{ext}"
+            file_path = os.path.join(folder_path, supp_file_name)
+
+        if os.path.isfile(file_path):
+            return file_path
+        else:
+            return "Not available for this raw file"
+
+
+
+    def _generate_examples(self, files, name):
+        if self.config.name == "device-data":
+            key = 0
+            data_dir = []
+            for folder in files:
+                tmp_dir = []
+                tmp_dir = self.get_all_file_paths(os.path.join(folder, DATA_DIR[name]))
+                data_dir = data_dir + tmp_dir
+
+            for path in data_dir:
+                res_dic = {}
+                for word in _DENSITY:
+                    if word in path:
+                        breast_density = word
+                        pattern = rf"(\d+\.\d+)_{word}"
+                        match = re.search(pattern, path)
+                        matched_text = match.group(1)
+                        break
+
+                # Get image id to filter the respective row of the csv
+                image_id = os.path.basename(path)
+                # Use os.path.splitext() to split the filename into root and extension
+                root, extension = os.path.splitext(image_id)
+                # Get the extension without the dot
+                image_labels = extension.lstrip(".")
+                res_dic["Raw"] = path
+                res_dic["mhd"] = self.get_support_file_path(path, "mhd")
+                res_dic["loc"] = self.get_support_file_path(path, "loc")
+                if self.config.name == "device-data":
+                    res_dic["dcm"] = self.get_support_file_path(path, "dcm")
+                res_dic["density"] = breast_density
+                res_dic["mass_radius"] = matched_text
+
+                yield key, res_dic
+                key += 1
+
+
+        if self.config.name == "segmentation-mask":
+            key = 0
+            data_dir = []
+            for folder in files:
+                tmp_dir = []
+                tmp_dir = self.get_all_file_paths(os.path.join(folder, DATA_DIR[name]))
+                data_dir = data_dir + tmp_dir
+
+            for path in data_dir:
+                res_dic = {}
+                for word in _DENSITY:
+                    if word in path:
+                        breast_density = word
+                        pattern = rf"(\d+\.\d+)_{word}"
+                        match = re.search(pattern, path)
+                        matched_text = match.group(1)
+                        break
+
+                # Get image id to filter the respective row of the csv
+                image_id = os.path.basename(path)
+                # Use os.path.splitext() to split the filename into root and extension
+                root, extension = os.path.splitext(image_id)
+                # Get the extension without the dot
+                image_labels = extension.lstrip(".")
+                res_dic["Raw"] = path
+                res_dic["mhd"] = self.get_support_file_path(path, "mhd")
+                res_dic["loc"] = self.get_support_file_path(path, "loc")
+                res_dic["density"] = breast_density
+                res_dic["mass_radius"] = matched_text
+
+                yield key, res_dic
+                key += 1
+            
+        if self.config.name == "metadata":
+            key = 0
+            examples = list()
+            meta_dir = os.path.join(files, DATA_DIR[name]) 
+
+            res_dic = {
+                "fatty": os.path.join(meta_dir,'bounds_fatty.npy'),
+                "dense": os.path.join(meta_dir,'bounds_dense.npy'),
+                "hetero": os.path.join(meta_dir,'bounds_hetero.npy'),                 
+                "scattered": os.path.join(meta_dir,'bounds_scattered.npy')
+            }
+            yield key, res_dic
+            key +=1