File size: 10,673 Bytes
6be63ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
e9b174a
6be63ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3e597e3
6be63ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
dd1630f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6be63ab
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bdf7c58
 
 
 
 
3e597e3
 
6be63ab
 
 
3e597e3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6be63ab
 
 
 
 
d0e336b
6be63ab
 
 
 
 
 
 
 
 
 
dd1630f
 
 
 
 
 
 
 
 
 
 
 
 
 
6be63ab
 
dd1630f
 
6be63ab
 
dd1630f
6be63ab
 
 
 
dd1630f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6be63ab
dd1630f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3e597e3
dd1630f
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 12 16:13:56 2024

@author: tominhanh
"""

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.

import pandas as pd
from PIL import Image as PilImage  # Import PIL Image with an alias
import datasets
from datasets import DatasetBuilder, GeneratorBasedBuilder, DownloadManager, DatasetInfo, Features, Image, ClassLabel, Value, Sequence, load_dataset, SplitGenerator
import os
import io
from typing import Tuple, Dict, List
import numpy as np
import zipfile
import requests
import random
from io import BytesIO
import csv

_CITATION = """\
https://arxiv.org/abs/2102.09099
"""

_DESCRIPTION = """\
The comprehensive dataset contains over 220,000 single-rater and multi-rater labeled nuclei from breast cancer images
obtained from TCGA, making it one of the largest datasets for nucleus detection, classification, and segmentation in hematoxylin and eosin-stained
digital slides of breast cancer. This version of the dataset is a revised single-rater dataset, featuring over 125,000 nucleus csvs.
These nuclei were annotated through a collaborative effort involving pathologists, pathology residents, and medical students, using the Digital Slide Archive.
"""

_HOMEPAGE = "https://sites.google.com/view/nucls/home?authuser=0"

_LICENSE = "CC0 1.0 license"

_URL = "https://www.dropbox.com/scl/fi/zsm9l3bkwx808wfryv5zm/NuCLS_dataset.zip?rlkey=x3358slgrxt00zpn7zpkpjr2h&dl=1"

class NuCLSDataset(GeneratorBasedBuilder):
    """The NuCLS dataset."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        """Returns the dataset info."""

        # Define the classes for the classifications
        raw_classification = ClassLabel(names=[
            'apoptotic_body', 'ductal_epithelium', 'eosinophil','fibroblast', 'lymphocyte',
            'macrophage', 'mitotic_figure', 'myoepithelium', 'neutrophil',
            'plasma_cell','tumor', 'unlabeled', 'vascular_endothelium'
        ])
        main_classification = ClassLabel(names=[
            'AMBIGUOUS', 'lymphocyte', 'macrophage', 'nonTILnonMQ_stromal',
            'plasma_cell', 'tumor_mitotic', 'tumor_nonMitotic',
        ])
        super_classification = ClassLabel(names=[
            'AMBIGUOUS','nonTIL_stromal','sTIL', 'tumor_any',
        ])
        type = ClassLabel(names=['rectangle', 'polyline'])

        # Define features
        features = Features({
            'rgb_image': Image(decode=True),
            'mask_image': Image(decode=True),
            'visualization_image': Image(decode=True),
            'annotation_coordinates': Features({
                'raw_classification': Sequence(Value("string")),
                'main_classification': Sequence(Value("string")),
                'super_classification': Sequence(Value("string")),
                'type': Sequence(Value("string")),
                'xmin': Sequence(Value('int64')),
                'ymin': Sequence(Value('int64')),
                'xmax': Sequence(Value('int64')),
                'ymax': Sequence(Value('int64')),
                'coords_x': Sequence(Sequence(Value('int64'))),  # Lists of integers
                'coords_y': Sequence(Sequence(Value('int64'))),  # Lists of integers
            })
        })
        return DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
          )

    def _split_generators(self, dl_manager: DownloadManager):
        # Download source data
        data_dir = dl_manager.download_and_extract(_URL)

        # Directory paths
        base_dir = os.path.join(data_dir, "NuCLS_dataset")
        rgb_dir = os.path.join(base_dir, "rgb")
        visualization_dir = os.path.join(base_dir, "visualization")
        mask_dir = os.path.join(base_dir, "mask")
        csv_dir = os.path.join(base_dir, "csv")
        split_dir = os.path.join(base_dir, "train_test_splits")

        # Generate a list of unique filenames (without extensions)
        unique_filenames = [os.path.splitext(f)[0] for f in os.listdir(rgb_dir)]

        # Process train/test split files to get slide names for each split and fold
        split_slide_names = self._process_train_test_split_files(split_dir)

        # Create the split generators for each fold
        split_generators = []
        for fold in split_slide_names:
            train_slide_names, test_slide_names = split_slide_names[fold]

            # Filter unique filenames based on slide names
            train_filenames = [fn for fn in unique_filenames if any(sn in fn for sn in train_slide_names)]
            test_filenames = [fn for fn in unique_filenames if any(sn in fn for sn in test_slide_names)]

            # Map filenames to file paths
            train_filepaths = self._map_filenames_to_paths(train_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir)
            test_filepaths = self._map_filenames_to_paths(test_filenames, rgb_dir, visualization_dir, mask_dir, csv_dir)

            # Add split generators for the fold
            split_generators.append(
                datasets.SplitGenerator(
                    name=f"{datasets.Split.TRAIN}_fold_{fold}",
                    gen_kwargs={"filepaths": train_filepaths}
                )
            )
            split_generators.append(
                datasets.SplitGenerator(
                    name=f"{datasets.Split.TEST}_fold_{fold}",
                    gen_kwargs={"filepaths": test_filepaths}
                )
            )

        return split_generators

    def _process_train_test_split_files(self, split_dir):
        """Reads the train/test split CSV files and returns a dictionary with fold numbers as keys and tuple of train/test slide names as values."""
        split_slide_names = {}
        for split_file in os.listdir(split_dir):
            file_path = os.path.join(split_dir, split_file)
            fold_number = split_file.split('_')[1]  # Assumes file naming format "fold_X_[train/test].csv"

            with open(file_path, 'r') as f:
                csv_reader = csv.reader(f)
                next(csv_reader)  # Skip header
                for row in csv_reader:
                    slide_name = row[1]  # Assuming slide_name is in the first column
                    if "train" in split_file:
                        split_slide_names.setdefault(fold_number, ([], []))[0].append(slide_name)
                    elif "test" in split_file:
                        split_slide_names.setdefault(fold_number, ([], []))[1].append(slide_name)

        return split_slide_names

    def _map_filenames_to_paths(self, filenames, rgb_dir, visualization_dir, mask_dir, csv_dir):
        """Maps filenames to file paths for each split."""
        filepaths = {}
        for filename in filenames:
            filepaths[filename] = {
                'rgb': os.path.join(rgb_dir, filename + '.png'),
                'visualization': os.path.join(visualization_dir, filename + '.png'),
                'mask': os.path.join(mask_dir, filename + '.png'),
                'csv': os.path.join(csv_dir, filename + '.csv'),
            }
        return filepaths

    def _generate_examples(self, filepaths):
        """Yield examples as (key, example) tuples."""

        for key, paths in filepaths.items():
            # Read the images using a method to handle the image files
            rgb_image = self._read_image_file(paths['rgb'])
            mask_image = self._read_image_file(paths['mask'])
            visualization_image = self._read_image_file(paths['visualization'])

            # Read the CSV and format the data as per the defined features
            annotation_coordinates = self._read_csv_file(paths['csv'])

            # Yield the example
            yield key, {
                'rgb_image': rgb_image,
                'mask_image': mask_image,
                'visualization_image': visualization_image,
                'annotation_coordinates': annotation_coordinates,
            }

    def _read_image_file(self, file_path: str, ) -> bytes:
        """Reads an image file and returns it as a bytes_like object."""
        try:
            with open(file_path, 'rb') as f:
                return f.read()
        except Exception as e:
            print(f"Error reading image file {file_path}: {e}")
            return None

    def _read_csv_file(self, filepath):
        """Reads the annotation CSV file and formats the data."""

        with open(filepath, 'r', encoding='utf-8') as csvfile:
            reader = csv.DictReader(csvfile)
            annotations = {
                'raw_classification': [],
                'main_classification': [],
                'super_classification': [],
                'type': [],
                'xmin': [],
                'ymin': [],
                'xmax': [],
                'ymax': [],
                'coords_x': [],
                'coords_y': []
            }

            for row in reader:
                annotations['raw_classification'].append(row.get('raw_classification', ''))
                annotations['main_classification'].append(row.get('main_classification', ''))
                annotations['super_classification'].append(row.get('super_classification', ''))
                annotations['type'].append(row.get('type', ''))
                annotations['xmin'].append(int(row.get('xmin', 0)))
                annotations['ymin'].append(int(row.get('ymin', 0)))
                annotations['xmax'].append(int(row.get('xmax', 0)))
                annotations['ymax'].append(int(row.get('ymax', 0)))

                # Handle coords_x and coords_y safely
                coords_x = row.get('coords_x', '')
                coords_y = row.get('coords_y', '')
                annotations['coords_x'].append([int(coord) if coord.isdigit() else 0 for coord in coords_x.split(',')])
                annotations['coords_y'].append([int(coord) if coord.isdigit() else 0 for coord in coords_y.split(',')])

            return annotations