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README.md

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+---
+license: cc-by-4.0
+pretty_name: CellImageNet
+task_categories:
+  - image-classification
+tags:
+  - biology
+  - single-cell
+  - cell-type-classification
+  - DAPI
+  - nuclear-morphology
+  - spatial-transcriptomics
+  - xenium
+size_categories:
+  - 1M<n<10M
+configs:
+  - config_name: human
+    data_files:
+      - split: full
+        path: data/human/*.tar
+  - config_name: mouse
+    data_files:
+      - split: full
+        path: data/mouse/*.tar
+---
+
+# CellImageNet
+
+**CellImageNet** is a large-scale single-cell image database of **paired DAPI
+nuclear images with cell-type annotations**, built from publicly available
+10x Genomics Xenium data. It contains **~10 million cells** from **42 Xenium
+samples — 28 human and 14 mouse tissues** — spanning diverse species, biological
+conditions, and tissue types, annotated with **31 harmonized cell-type classes**
+(unified from the source datasets' own annotations into a common label set).
+
+Each cell has paired DAPI crops centered on the same cell at complementary context scales:
+
+- **2.5×** — a tight view capturing fine nuclear morphology, and
+- **10×** — a wider view capturing the local tissue context / neighbourhood.
+
+Crops are provided at their **native resolution** (variable per sample; they are
+*not* pre-resized — resize to a fixed input size, e.g. 224×224, is left to the
+downstream model).
+
+## Configurations & splits
+
+| config | content |
+|---|---|
+| `human` | 28 human Xenium samples (~6.5M cells) |
+| `mouse` | 14 mouse Xenium samples (~3.4M cells) |
+
+(Counts are pre-filtering segmentation totals; the released set is marginally
+smaller after removing cells with tiny nuclear masks or missing crops.)
+
+This is an unsplit corpus: each config exposes a single `full` split (we do not
+ship an official train/test partition). The exact subset used to train MorphPT
+is specified in the [MorphPT weights repo](https://huggingface.co/jilab/MorphPT)
+under `splits/`.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("jilab/CellImageNet", "human", split="full", streaming=True)
+ex = next(iter(ds))
+ex["2p5x.png"], ex["10x.png"], ex["json"]["cell_type"]
+```
+
+## Sample schema (WebDataset)
+
+Each sample (one cell) is keyed by `cell_id` with three members:
+
+| member | type | description |
+|---|---|---|
+| `2p5x.png` | image | 2.5× DAPI crop (grayscale, native resolution) |
+| `10x.png` | image | 10× DAPI crop (grayscale, native resolution) |
+| `json` | dict | metadata (below) |
+
+`json` fields: `cell_id`, `dataset` (source Xenium sample), `species`
+(Human/Mouse), `tissue`, `condition`, `cell_type` (one of the 31 classes below,
+plus a small `Unknown` bucket in some mouse samples), `x_centroid`, `y_centroid`
+(nuclear centroid, **microns**).
+
+> Note: the field is named `cell_type` (the fine-grained cell label). It is
+> *not* the coarse morphology "group" used by the MorphPT router — that grouping
+> lives in the model repo, not in this dataset.
+
+## Cell-type classes
+
+The 31 harmonized cell-type labels in `cell_type`:
+
+<details>
+<summary>All 31 classes</summary>
+
+Astrocytes · B cells · Brain cancer cells · Cardiac muscle cells · Chondrocytes ·
+Colon cancer cells · Endothelial cells · Ependymal cells · Epithelial cells ·
+Erythrocytes · Fibroblasts · Kidney cancer cells · Liver cancer cells ·
+Lung cancer cells · Mesangial cells · Microglia · Myeloid cells · NK cells ·
+Neurons · OPCs · Oligodendrocytes · Ovary cancer cells · Pancreas cancer cells ·
+Pericytes · Schwann cells · Skeletal muscle cells · Skin cancer cells ·
+Smooth muscle cells · Stem and progenitor cells · Stromal cells · T cells
+
+</details>
+
+`Unknown` is **mouse-only** (~134k cells, ≈3.8% of the mouse split; no human cell
+carries it) and marks cells left unannotated in the source. Filter it out if you
+need a clean 31-class label space.
+
+## How it was built
+
+Source: 42 Xenium samples (28 human, 14 mouse) from the
+[10x Genomics datasets portal](https://www.10xgenomics.com/datasets). From each
+tissue-wide DAPI image we used the `morphology_mip` maximum-intensity-projection
+channel (or `morphology_focus` when unavailable). Nuclear segmentation masks
+(10x Xenium Onboard Analysis) were converted to pixels at 0.2125 µm/px; cells
+with rasterized nuclear area < 5 px or a bounding box < 10 px in either dimension
+were removed. For each cell, two square crops centred on the nuclear centroid
+were extracted at context scales r = 2.5 and r = 10 (side length S_r = r·d, with
+d the per-sample mean nuclear bounding-box size) and zero-padded at image
+borders. Crops are stored at native resolution.
+
+## License & attribution
+
+CellImageNet is a **derivative work** of publicly available 10x Genomics Xenium
+datasets. The underlying imaging data is distributed by 10x Genomics under the
+**Creative Commons Attribution 4.0 International ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))**
+license. Because CellImageNet is derived from CC BY 4.0 material, the image crops
+are released under **CC BY 4.0**; the cell-type annotations and derived metadata
+contributed by the CellImageNet authors are likewise released under CC BY 4.0.
+See [`LICENSE`](LICENSE) for the full statement.
+
+Under CC BY 4.0 you may share and adapt this dataset, including commercially,
+provided you (1) credit 10x Genomics and the CellImageNet authors, (2) link the
+license, and (3) **indicate that changes were made** — the images here have been
+cropped/re-framed and re-annotated and are **not** the original 10x Genomics
+files.
+
+### Source datasets
+
+All 42 source samples are 10x Genomics Xenium In Situ datasets from the
+[10x Genomics datasets portal](https://www.10xgenomics.com/datasets). Each is
+individually licensed CC BY 4.0 on its dataset page. The complete list of source
+samples (dataset name, species, tissue, condition, and its 10x dataset URL) is
+provided in **[`attribution_manifest.csv`](attribution_manifest.csv)** in this
+repository.
+
+Please cite both 10x Genomics and the individual source datasets in addition to
+the CellImageNet/MorphPT paper below.
+
+## Limitations
+
+- **DAPI only** — nuclear morphology, no gene expression or protein channels
+  (despite deriving from Xenium spatial-transcriptomics runs).
+- **Native-resolution crops** vary in pixel size across samples; downstream
+  models must resize to a fixed input.
+- **Unsplit and imbalanced** — no official train/test split, and class frequency
+  is highly skewed (tissue/condition sampling reflects the source datasets, not a
+  balanced design). Subsample or reweight for classifier training.
+- Labels are the source annotations harmonized into 31 classes; ≈3.8% of mouse
+  cells (none in human) are `Unknown`.
+
+## Relation to MorphPT
+
+CellImageNet is the training corpus for **MorphPT**, a visual foundation model
+for cell classification. MorphPT was trained on a human-only, per-class
+subsampled subset of CellImageNet.
+
+- Code: <https://github.com/AnitaCao/MorphPT>
+- Model weights: <https://huggingface.co/jilab/MorphPT>
+
+
+## Citation
+
+```bibtex
+@article{cao2026visual,
+  title   = {A visual foundation model for cell classification},
+  author  = {Cao, Ting and Zhuang, Haotian and Zhang, Boxuan and
+             Pang, Zhiping P. and Tang, Ruixiang and Liu, Dongfang and
+             Ji, Zhicheng},
+  year    = {2026}
+}
+```