Add validated PanNuke folds, dataset card and annotation examples

README.md

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+---
+license: cc-by-nc-sa-4.0
+task_categories:
+  - image-segmentation
+tags:
+  - histology
+  - nuclei
+  - instance-segmentation
+configs:
+  - config_name: default
+    data_files:
+      - split: fold1
+        path: fold_1.parquet
+      - split: fold2
+        path: fold_2.parquet
+      - split: fold3
+        path: fold_3.parquet
+---
+
+# PanNuke in Parquet format
+
+[PanNuke](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke/) contains
+256 × 256 histology images with labels for individual nuclei and their cell types.
+This version stores all 7,901 images in Parquet files, keeping the original three
+folds and cell-type mapping. It is a repackaging, not an official author release.
+
+## Files and fields
+
+There is one Parquet file per fold and one row per image. The `image`, `inst`
+and `type` fields contain PNG bytes.
+
+| Field | Contents |
+| --- | --- |
+| `id` | Unique sample name: `fold1_00000`, for example |
+| `image` | RGB uint8 image encoded as PNG; pixel values are 0–255 |
+| `inst` | uint16 instance mask encoded as PNG; 0 is background |
+| `type` | uint8 cell-type mask encoded as PNG; labels are listed below |
+| `fold` | Original fold number, 1, 2 or 3 |
+| `tissue` | Original tissue category from `types.npy` |
+
+## Cell types
+
+The `type` mask uses these labels:
+
+| Value | Class |
+| --- | --- |
+| 0 | background |
+| 1 | neoplastic |
+| 2 | inflammatory |
+| 3 | connective |
+| 4 | dead |
+| 5 | epithelial |
+
+Instance IDs start at 1 in each image; 0 is background. IDs are unique within
+an image and have no meaning across images.
+
+## Original folds
+
+Keep the original folds for comparisons with published PanNuke results. The
+authors specify these three arrangements:
+
+| Arrangement | Training | Validation | Testing |
+| --- | --- | --- | --- |
+| 1 | Fold 1 | Fold 2 | Fold 3 |
+| 2 | Fold 2 | Fold 1 | Fold 3 |
+| 3 | Fold 3 | Fold 2 | Fold 1 |
+
+## Read a sample
+
+Download a fold Parquet file, then decode one sample:
+
+```python
+from io import BytesIO
+
+import numpy as np
+import polars as pl
+from PIL import Image
+
+
+def read_png(data: bytes) -> np.ndarray:
+    """Read an image or label mask from PNG bytes."""
+    with Image.open(BytesIO(data)) as image:
+        return np.array(image)
+
+
+row = pl.read_parquet("fold_1.parquet", n_rows=1).row(0, named=True)
+image = read_png(row["image"])
+instances = read_png(row["inst"])
+cell_types = read_png(row["type"])
+```
+
+The decoded shapes are `(256, 256, 3)` for the RGB image and `(256, 256)` for
+both masks. Each positive instance number identifies one nucleus; `type` gives
+its cell category at the same pixels. The Hub configuration exposes `fold1`,
+`fold2` and `fold3` as separate splits.
+
+## Plot patches and annotations
+
+The following example shows three patches from fold 1:
+
+```python
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+from skimage.segmentation import find_boundaries
+
+sample_ids = ["fold1_00017", "fold1_00738", "fold1_02422"]
+samples = (
+    pl.scan_parquet("fold_1.parquet")
+    .filter(pl.col("id").is_in(sample_ids))
+    .collect()
+)
+names = ["Neoplastic", "Inflammatory", "Connective", "Dead", "Epithelial"]
+colors = np.array([
+    [230, 75, 53], [54, 140, 204], [0, 160, 135],
+    [243, 155, 60], [144, 102, 190],
+]) / 255
+
+fig, axes = plt.subplots(3, 2, figsize=(5.5, 8))
+for row_axes, sample in zip(axes, samples.iter_rows(named=True)):
+    image = read_png(sample["image"])
+    instances = read_png(sample["inst"])
+    cell_types = read_png(sample["type"])
+    foreground = instances > 0
+    overlay = image / 255
+    overlay[foreground] = (
+        0.65 * overlay[foreground] + 0.35 * colors[cell_types[foreground] - 1]
+    )
+    edges = find_boundaries(instances, mode="inner") & foreground
+    overlay[edges] = colors[cell_types[edges] - 1]
+    for ax, pixels in zip(row_axes, (image, overlay)):
+        ax.imshow(pixels)
+        ax.set_axis_off()
+    row_axes[0].set_title(sample["tissue"], fontsize=9)
+
+axes[0, 1].set_title("Nucleus annotations", fontsize=9)
+fig.legend(
+    handles=[Patch(facecolor=c, label=n) for c, n in zip(colors, names)],
+    loc="lower center", ncol=5, frameon=False, fontsize=7,
+)
+fig.tight_layout(rect=(0, 0.04, 1, 1))
+plt.show()
+```
+
+![Three PanNuke patches with cell-type annotation overlays](examples.png)
+
+Breast, colon and skin patches from fold 1. Left: original images. Right:
+annotated nuclei, coloured by cell type.
+
+## Conversion notes
+
+Images keep their original pixel values and size. Instance IDs are renumbered
+within each image. A small number of pixels belong to multiple nucleus class
+channels in the source data; these are assigned to the lower-numbered class.
+Background is any pixel without a nucleus. Every original nucleus is retained.
+
+All saved images and masks are checked against the source after conversion.
+`manifest.json` records sample counts, conversion details and file hashes.
+
+## License and attribution
+
+PanNuke is provided for research and noncommercial use under
+[CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+This repackaging retains that license. Attribute the original authors, cite both
+papers below, link to the original data and license, and identify the format and
+label-conversion changes when redistributing it.
+
+- Gamper et al. (2019). *PanNuke: an open pan-cancer histology dataset for nuclei
+  instance segmentation and classification.*
+  [Paper](https://doi.org/10.1007/978-3-030-23937-4_2).
+- Gamper et al. (2020). *PanNuke Dataset Extension, Insights and Baselines.*
+  [Paper](https://arxiv.org/abs/2003.10778).
+
+## Validated sample counts
+
+| Fold | Samples |
+| --- | --- |
+| 1 | 2656 |
+| 2 | 2523 |
+| 3 | 2722 |
+
+Total: 7901 samples.

fold_1.parquet

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+oid sha256:b9e40907a07c59768c674d142ea80b4dd867b29bc70dcb0f8bef4e9c446b9a79
+size 329319785

fold_2.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:413de87c2c3e130f28086c8b5e03075583e8e90fd6ba3a4e254315ce86e1de6b
+size 309704351

fold_3.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:98327000b6203ac08c7a80288024653049ab764b203b1dd133fee9c467440001
+size 338978966

manifest.json

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+{
+  "created_utc": "2026-10-18T08:27:08.417852+00:00",
+  "source": "https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke",
+  "license": "cc-by-nc-sa-4.0",
+  "classes": {
+    "0": "background",
+    "1": "neoplastic",
+    "2": "inflammatory",
+    "3": "connective",
+    "4": "dead",
+    "5": "epithelial"
+  },
+  "overlap_policy": "lowest nucleus class number wins; background is pixels without nuclei",
+  "conversion_script_sha256": "6bcf46f2afd8dfb8f0de7e99d685cb564131cd73dd411e2089007ba9f886cdb8",
+  "folds": [
+    {
+      "fold": 1,
+      "samples": 2656,
+      "nuclei_by_class": {
+        "neoplastic": 26201,
+        "inflammatory": 10820,
+        "connective": 16388,
+        "dead": 967,
+        "epithelial": 8842
+      },
+      "samples_by_tissue": {
+        "Breast": 827,
+        "Colon": 478,
+        "Lung": 80,
+        "Kidney": 37,
+        "Prostate": 77,
+        "Bladder": 21,
+        "Stomach": 50,
+        "Ovarian": 54,
+        "Esophagus": 155,
+        "Pancreatic": 78,
+        "Uterus": 17,
+        "Thyroid": 80,
+        "Skin": 59,
+        "Cervix": 155,
+        "Adrenal_gland": 134,
+        "Bile-duct": 159,
+        "Testis": 60,
+        "HeadNeck": 70,
+        "Liver": 65
+      },
+      "overlap_pixels": 1216,
+      "samples_with_overlap": 194,
+      "source_background_mismatch_pixels": 2359296,
+      "source_url": "https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke/fold_1.zip",
+      "archive_sha256": "6e19ad380300e8ce9480f9ab6a14cc91fa4b6a511609b40e3d70bdf9c881ed0b",
+      "parquet_sha256": "b9e40907a07c59768c674d142ea80b4dd867b29bc70dcb0f8bef4e9c446b9a79",
+      "parquet": "fold_1.parquet",
+      "validation": "all saved RGB pixels and resolved class/instance masks match source; no nuclei lost"
+    },
+    {
+      "fold": 2,
+      "samples": 2523,
+      "nuclei_by_class": {
+        "neoplastic": 22731,
+        "inflammatory": 10631,
+        "connective": 16756,
+        "dead": 884,
+        "epithelial": 8870
+      },
+      "samples_by_tissue": {
+        "Breast": 749,
+        "Colon": 468,
+        "Lung": 53,
+        "Kidney": 56,
+        "Prostate": 37,
+        "Bladder": 61,
+        "Stomach": 48,
+        "Ovarian": 40,
+        "Esophagus": 128,
+        "Pancreatic": 89,
+        "Uterus": 6,
+        "Thyroid": 84,
+        "Skin": 87,
+        "Cervix": 52,
+        "Adrenal_gland": 148,
+        "Bile-duct": 103,
+        "Testis": 79,
+        "HeadNeck": 169,
+        "Liver": 66
+      },
+      "overlap_pixels": 1572,
+      "samples_with_overlap": 190,
+      "source_background_mismatch_pixels": 3801371,
+      "source_url": "https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke/fold_2.zip",
+      "archive_sha256": "5bc540cc509f64b5f5a274d6e5a245527dbd3e6d3155d43555115c5d54709b07",
+      "parquet_sha256": "413de87c2c3e130f28086c8b5e03075583e8e90fd6ba3a4e254315ce86e1de6b",
+      "parquet": "fold_2.parquet",
+      "validation": "all saved RGB pixels and resolved class/instance masks match source; no nuclei lost"
+    },
+    {
+      "fold": 3,
+      "samples": 2722,
+      "nuclei_by_class": {
+        "neoplastic": 28471,
+        "inflammatory": 10825,
+        "connective": 17441,
+        "dead": 1057,
+        "epithelial": 8860
+      },
+      "samples_by_tissue": {
+        "Breast": 775,
+        "Colon": 494,
+        "Lung": 51,
+        "Kidney": 41,
+        "Prostate": 68,
+        "Bladder": 64,
+        "Stomach": 48,
+        "Ovarian": 52,
+        "Esophagus": 141,
+        "Uterus": 163,
+        "Thyroid": 62,
+        "Skin": 41,
+        "Cervix": 86,
+        "Adrenal_gland": 155,
+        "Pancreatic": 28,
+        "Bile-duct": 158,
+        "Testis": 57,
+        "HeadNeck": 145,
+        "Liver": 93
+      },
+      "overlap_pixels": 1530,
+      "samples_with_overlap": 191,
+      "source_background_mismatch_pixels": 4325424,
+      "source_url": "https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke/fold_3.zip",
+      "archive_sha256": "c14d372981c42f611ebc80afad01702b89cad8c1b3089daa31931cf5a4b1a39d",
+      "parquet_sha256": "98327000b6203ac08c7a80288024653049ab764b203b1dd133fee9c467440001",
+      "parquet": "fold_3.parquet",
+      "validation": "all saved RGB pixels and resolved class/instance masks match source; no nuclei lost"
+    }
+  ]
+}