Duplicate from RationAI/PanNuke

Co-authored-by: Matěj Pekár <matejpekar@users.noreply.huggingface.co>

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README.md

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+---
+dataset_info:
+  features:
+  - name: image
+    dtype:
+      image:
+        mode: RGB
+  - name: instances
+    sequence:
+      image:
+        mode: '1'
+  - name: categories
+    sequence:
+      class_label:
+        names:
+          '0': Neoplastic
+          '1': Inflammatory
+          '2': Connective
+          '3': Dead
+          '4': Epithelial
+  - name: tissue
+    dtype:
+      class_label:
+        names:
+          '0': Adrenal Gland
+          '1': Bile Duct
+          '2': Bladder
+          '3': Breast
+          '4': Cervix
+          '5': Colon
+          '6': Esophagus
+          '7': Head & Neck
+          '8': Kidney
+          '9': Liver
+          '10': Lung
+          '11': Ovarian
+          '12': Pancreatic
+          '13': Prostate
+          '14': Skin
+          '15': Stomach
+          '16': Testis
+          '17': Thyroid
+          '18': Uterus
+  splits:
+  - name: fold1
+    num_bytes: 283673837.64
+    num_examples: 2656
+  - name: fold2
+    num_bytes: 267595457.439
+    num_examples: 2523
+  - name: fold3
+    num_bytes: 293079722.82
+    num_examples: 2722
+  download_size: 1665092597
+  dataset_size: 844349017.8989999
+configs:
+- config_name: default
+  data_files:
+  - split: fold1
+    path: data/fold1-*
+  - split: fold2
+    path: data/fold2-*
+  - split: fold3
+    path: data/fold3-*
+license: cc-by-nc-sa-4.0
+task_categories:
+- image-segmentation
+task_ids:
+- instance-segmentation
+language:
+- en
+tags:
+- medical
+- cell nuclei
+- H&E
+pretty_name: PanNuke
+size_categories:
+- 1K<n<10K
+paperswithcode_id: pannuke
+---
+
+# PanNuke
+
+[![](https://github.com/Mr-TalhaIlyas/Prerpcessing-PanNuke-Nuclei-Instance-Segmentation-Dataset/blob/master/screens/img1.png?raw=true)](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke)
+
+## Dataset Description
+
+- **Homepage:** [PanNuke Dataset for Nuclei Instance Segmentation and Classification](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke)
+- **Leaderboard:** [Panoptic Segmentation](https://paperswithcode.com/sota/panoptic-segmentation-on-pannuke)
+
+## Description
+
+PanNuke is a semi-automatically generated dataset for nuclei instance segmentation and classification, providing comprehensive nuclei annotations across 19 tissue types and 5 distinct cell categories. The dataset includes a total of **189,744 labeled nuclei**, each accompanied by an instance segmentation mask, and contains **7,901 images**, each sized **256×256 pixels**. The images were captured at **x40 magnification** with a resolution of **0.25 µm/pixel**. The dataset is highly imbalanced, with the **"Dead" nuclei category** being particularly underrepresented.
+
+Please note that the dataset was created by extracting patches from whole-slide images (WSIs). As a result, some nuclei located at the edges of patches may be cropped, with fewer than 10 visible pixels in certain cases.
+
+## Dataset Structure
+
+The dataset is organized into three folds: `fold1`, `fold2`, and `fold3`, consistent with the original dataset structure. Each fold contains data in a tabular format with the following four columns:
+
+- **`image`**: The RGB tile of the sample.
+- **`instances`**: A list of nuclei instances. Each instance represents exactly one nucleus and is in binary format (`1` - nucleus, `0` - background)
+- **`categories`**: An integer class label for each nucleus, corresponding to one of the following categories:
+  0. Neoplastic
+  1. Inflammatory
+  2. Connective
+  3. Dead
+  4. Epithelial
+- **`tissue`**: The integer tissue type from which the sample originates, belonging to one of these categories:
+  0. Adrenal Gland
+  1. Bile Duct
+  2. Bladder
+  3. Breast
+  4. Cervix
+  5. Colon
+  6. Esophagus
+  7. Head & Neck
+  8. Kidney
+  9. Liver
+  10. Lung
+  11. Ovarian
+  12. Pancreatic
+  13. Prostate
+  14. Skin
+  15. Stomach
+  16. Testis
+  17. Thyroid
+  18. Uterus
+
+## Citation
+
+```bibtex
+@inproceedings{gamper2019pannuke,
+  title={PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification},
+  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Benes, Ksenija and Khuram, Ali and Rajpoot, Nasir},
+  booktitle={European Congress on Digital Pathology},
+  pages={11--19},
+  year={2019},
+  organization={Springer}
+}
+```
+
+```bibtex
+@article{gamper2020pannuke,
+  title={PanNuke Dataset Extension, Insights and Baselines},
+  author={Gamper, Jevgenij and Koohbanani, Navid Alemi and Graham, Simon and Jahanifar, Mostafa and Khurram, Syed Ali and Azam, Ayesha and Hewitt, Katherine and Rajpoot, Nasir},
+  journal={arXiv preprint arXiv:2003.10778},
+  year={2020}
+}
+```

data/fold1-00000-of-00001.parquet

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gen_script.py

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+from collections.abc import Generator
+from pathlib import Path
+from typing import Any
+
+import datasets
+import numpy as np
+from datasets import Dataset
+from datasets.splits import NamedSplit
+from numpy.typing import NDArray
+from PIL import Image
+from tqdm import tqdm
+
+
+tissue_map = {
+    "Bile-duct": "Bile Duct",
+    "HeadNeck": "Head & Neck",
+    "Adrenal_gland": "Adrenal Gland",
+}
+
+features = datasets.Features(
+    {
+        "image": datasets.Image(mode="RGB"),
+        "instances": datasets.Sequence(datasets.Image(mode="1")),
+        "categories": datasets.Sequence(
+            datasets.ClassLabel(
+                num_classes=5,
+                names=[
+                    "Neoplastic",
+                    "Inflammatory",
+                    "Connective",
+                    "Dead",
+                    "Epithelial",
+                ],
+            )
+        ),
+        "tissue": datasets.ClassLabel(
+            num_classes=19,
+            names=[
+                "Adrenal Gland",
+                "Bile Duct",
+                "Bladder",
+                "Breast",
+                "Cervix",
+                "Colon",
+                "Esophagus",
+                "Head & Neck",
+                "Kidney",
+                "Liver",
+                "Lung",
+                "Ovarian",
+                "Pancreatic",
+                "Prostate",
+                "Skin",
+                "Stomach",
+                "Testis",
+                "Thyroid",
+                "Uterus",
+            ],
+        ),
+    }
+)
+
+
+def one_hot_mask(
+    mask: NDArray[np.float64],
+) -> tuple[NDArray[np.bool], NDArray[np.uint8]]:
+    """Converts a mask to one-hot encoding.
+
+    Returns:
+        A dictionary with the following keys:
+            - masks: A 3D array with shape (num_masks, height, width) containing the
+                one-hot encoded masks.
+            - labels: A 1D array with shape (num_masks,) containing the class labels.
+    """
+    masks: list[NDArray[np.bool]] = []
+    labels: list[NDArray[np.uint8]] = []
+
+    for c in range(mask.shape[-1] - 1):
+        masks.append(mask[..., c] == np.unique(mask[..., c])[1:, None, None])
+        labels.append(np.full(masks[-1].shape[0], c, dtype=np.uint8))
+
+    return np.concatenate(masks), np.concatenate(labels)
+
+
+def process(path: str, subfolder: str) -> Generator[dict[str, Any], None, None]:
+    images = np.load(Path(path, "images", subfolder, "images.npy"), mmap_mode="r")
+    masks = np.load(Path(path, "masks", subfolder, "masks.npy"), mmap_mode="r")
+    types = np.load(Path(path, "images", subfolder, "types.npy"))
+
+    for image, mask, tissue in tqdm(
+        zip(images, masks, types, strict=True), total=len(images)
+    ):
+        mask, labels = one_hot_mask(mask)
+
+        yield {
+            "image": Image.fromarray(image.astype(np.uint8)),
+            "instances": [Image.fromarray(m) for m in mask],
+            "categories": labels,
+            "tissue": tissue_map.get(tissue, tissue),
+        }
+
+
+if __name__ == "__main__":
+    fold1 = Dataset.from_generator(
+        process,
+        gen_kwargs={"path": "PanNuke/Fold 1", "subfolder": "fold1"},
+        features=features,
+        split=NamedSplit("fold1"),
+        keep_in_memory=True,
+    )
+    fold1.push_to_hub("RationAI/PanNuke")
+    fold2 = Dataset.from_generator(
+        process,
+        gen_kwargs={"path": "PanNuke/Fold 2", "subfolder": "fold2"},
+        features=features,
+        split=NamedSplit("fold2"),
+        keep_in_memory=True,
+    )
+    fold2.push_to_hub("RationAI/PanNuke")
+    fold3 = Dataset.from_generator(
+        process,
+        gen_kwargs={"path": "PanNuke/Fold 3", "subfolder": "fold3"},
+        features=features,
+        split=NamedSplit("fold3"),
+        keep_in_memory=True,
+    )
+    fold3.push_to_hub("RationAI/PanNuke")