CAMEO-Lung: A Multimodal Benchmark Dataset of Aligned H&E Patches and Gene Expression Profiles in the Lung

Citation

If you use this dataset, please cite it directly and the original lung study:

@dataset{kuijs_cameo_lung_2026,
  author    = {Kuijs, Merel and Richter, Till and Gindra, Rushin H and Traeuble, Korbinian and
               Matek, Christian and Lukn{\'a}rov{\'a}, Rebeka and Peng, Tingying and Theis, Fabian J},
  title     = {CAMEO-Lung: A Multimodal Benchmark Dataset of Aligned H&E Patches and Gene Expression Profiles in the Lung},
  year      = {2026},
  publisher = {Hugging Face},
  doi       = {10.57967/hf/7910},
  url       = {https://huggingface.co/datasets/theislab/CAMEO-Lung}
}

@article{lpf,
  title   = {Spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis},
  author  = {Vannan, Annika and Lyu, Ruqian and Williams, Arianna L and Negretti, Nicholas M and Mee, Evan D and Hirsh, Joseph and Hirsh, Samuel and Hadad, Niran and Nichols, David S and Calvi, Carla L and others},
  journal = {Nature Genetics},
  volume  = {57},
  number  = {3},
  pages   = {647--658},
  year    = {2025}
}

---

Dataset Description

This dataset is part of the CAMEO framework for multimodal spatial transcriptomics learning. It contains paired histology images and gene expression data derived from the Lung Pulmonary Fibrosis (LungPF) 10x Xenium cohort, comprising 23 samples from 19 patients covering healthy lung tissue and varying severities of pulmonary fibrosis.

Each row represents one niche — a 224×224 pixel crop of an H&E-stained histology slide paired with the single-cell gene expression profiles of all cells located within that crop, together with expert pathologist niche annotations, per-cell coordinates, and cell-type composition. In total, the dataset contains 71,309 niches encompassing approximately 1 million cells across 23 samples. We constructed these niche-level paired representations by spatially aligning the histological and transcriptomic modalities using SpatialData, tessellating non-overlapping crops across each slide, and applying a quality control filter to exclude niches with less than 50% tissue coverage. Transcripts from partially included cells are treated as whole-cell data within the niche. Broad cell-type annotations (10 categories) and niche-level annotations were both provided by the original authors.

In addition to raw modality data, the dataset includes a set of precomputed embeddings from several unimodal foundation models to facilitate research on multimodal and unimodal representation learning.

• Organization: Theislab
• Source data: Lung Pulmonary Fibrosis (LungPF) Xenium cohort
• License: CC BY-NC-SA 4.0

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Dataset Structure

Splits

The dataset is stored as a single train split containing all 71,309 niches across all 23 samples.

Split	Niches
Full dataset (train)	71,309

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Column Descriptions

Each row corresponds to one niche (224×224 px patch). The following columns are included:

Identifiers and labels

Column	Type	Description
name	string	Sample (slide) identifier, e.g. "VUILD102LF". Maps to one of the 23 Xenium samples.
annotation	ClassLabel (int64)	Expert pathologist niche annotation, encoded as an integer. See Niche Label Mapping below.
species	ClassLabel (int64)	Species label. Always 0 (human) in this cohort.
cancer	ClassLabel (int64)	Cancer flag. Always 0 (False) in this cohort.
tissue	ClassLabel (int64)	Tissue label. Always 0 (lung) in this cohort.

Raw modality data

Column	Type	Shape	Description
image	Image	224×224 RGB	H&E-stained histology patch.
gexp	Array2D float32	(200, 343)	Raw gene expression counts per cell. Up to 200 cells per niche (zero-padded); 343 Xenium panel genes. Use mask to identify valid cells.
spot_gexp	Array2D float32	(1, 343)	Niche-level pseudobulk gene expression (sum over valid cells).
mask	Sequence bool	(200,)	Boolean mask indicating valid cells (True = real cell, False = padding).
cell_coords	Array2D int32	(200, 2)	Cell centroid coordinates (x, y) in pixel space within the 224×224 patch. Padded to 200 rows.
cell_type_ratio	Sequence float32	(10,)	Fraction of each of the 10 broad cell types present in the niche.

Precomputed embeddings

All embeddings are niche-level representations derived from the raw modalities.

Column	Type	Shape	Description
img_embed	Sequence float32	(1024,)	Image embedding from UNI
conch_embedding	Sequence float64	(512,)	Image embedding from CONCH
ctranspath_embedding	Sequence float64	(768,)	Image embedding from CTransPath.
gexp_embed	Sequence float32	(128,)	Gene expression embedding learned by a self-supervised Graph Attention Network.
scvi_pool	Sequence float64	(128,)	scVI embedding, pooled over valid cells in the niche.
scvi_pseudobulk	Sequence float64	(128,)	scVI embedding computed from the pseudobulk niche expression profile.
pca_pool	Sequence float64	(128,)	PCA embedding (128 components), pooled over valid cells in the niche.
pca_pseudobulk	Sequence float64	(128,)	PCA embedding computed from the pseudobulk niche expression profile.
nicheformer_pool	Sequence float64	(512,)	Nicheformer embedding, pooled over valid cells in the niche.
scgpt_pool	Sequence float64	(512,)	scGPT embedding, pooled over valid cells in the niche.

Niche Label Mapping

The annotation column contains integer class labels corresponding to expert-annotated niche types:

Integer	Niche type
0	Advanced Remodeling
1	Airway Smooth Muscle
2	Artery
3	Emphysema
4	Fibroblastic Focus
5	Fibrosis
6	Giant Cell
7	Goblet Cell Metaplasia
8	Granuloma
9	Hyperplastic AECs
10	Interlobular Septum
11	Large Airway
12	Microscopic Honeycombing
13	Minimally Remodeled Alveoli
14	Mixed Inflammation
15	Muscularized Artery
16	NOANNOT
17	Normal Alveoli
18	Remnant Alveoli
19	Remodeled Epithelium
20	Severe Fibrosis
21	Small Airway
22	TLS
23	Venule

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Loading the Dataset

Standard loading

from datasets import load_dataset

dataset = load_dataset("theislab/CAMEO-Lung")
train_ds = dataset["train"]

# Access one example
example = train_ds[0]
print(example.keys())
# dict_keys(['name', 'image', 'img_embed', 'gexp_embed', 'cell_type_ratio',
#            'annotation', 'species', 'cancer', 'tissue', 'gexp', 'mask',
#            'cell_coords', 'spot_gexp', 'conch_embedding', 'ctranspath_embedding',
#            'pca_pool', 'pca_pseudobulk', 'scvi_pool', 'scvi_pseudobulk',
#            'nicheformer_pool', 'scgpt_pool'])

# The image is a PIL Image
print(example["image"].size)       # (224, 224)

# Gene expression: shape (200, 343), use mask to select valid cells
import numpy as np
gexp = np.array(example["gexp"])         # shape (200, 343)
mask = np.array(example["mask"])         # shape (200,) bool
gexp_valid = gexp[mask]                  # shape (n_cells, 343)

# Decode the niche label
label_name = train_ds.features["annotation"].int2str(example["annotation"])
print(label_name)  # e.g. "Normal Alveoli"

Streaming (avoids downloading all ~25 GB upfront)

from datasets import load_dataset

dataset = load_dataset("theislab/CAMEO-Lung", streaming=True)
for example in dataset["train"]:
    # process one niche at a time
    break

Filtering by sample

train_samples = ["VUILD102LF", ...]
train_split = dataset["train"].filter(lambda x: x["name"] in train_samples)

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License

This dataset is distributed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.