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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://production-media.paperswithcode.com/datasets/eb89f34e-880b-4ab0-9d9b-75d7b6bf3159.png)](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 nuclei instance segmentation and classification dataset with exhaustive nuclei labels across 19 different tissue types. In total the dataset contains 189,744 labeled nuclei, each with an instance segmentation mask.
+
+## 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")