HPA10M Dataset

A large-scale immunohistochemistry (IHC) image dataset derived from the Human Protein Atlas (HPA, https://www.proteinatlas.org/), containing approximately 10.5 million pathology and tissue images with detailed annotations.

Dataset Overview

Statistic	Value
Total Images	10,495,672
Training Set	10,493,672 images (10,497 tar files)
Validation Set	2,000 images (1 tar file)
Image Types	Pathology (7,970,595) / Tissue (2,525,077)
Format	JPEG images + JSON metadata

Directory Structure

hpa10m/
├── README.md                              # This file
├── example_images/                        # Sample images for preview
├── hpa10m_train/                          # Training data (WebDataset tar files)
│   ├── hpa10m_train_0000.tar             # Training shards (10,497 files)
│   ├── hpa10m_train_0001.tar
│   ├── ...
├── hpa10m_validation/                     # Validation data
│   └── hpa10m_validation.tar              # All validation samples (2,000 images)
└── hpa10m_tar_summary/                    # Metadata index files
    └── all.feather                        # Complete index of all images

Data Format

Tar Archives (WebDataset Format)

Each tar file contains paired .jpg and .json files organized by:

• Image category: pathology/ or tissue/
• Gene prefix: Two-letter gene name prefix (e.g., AB/, CD/)

JSON Metadata Structure

Each image has a corresponding JSON file with rich annotations:

{
  "metadata": {
    "height": 3000,
    "width": 3000,
    "name": "image_filename.jpg",
    "format": ".jpg"
  },
  "custom_metadata": {
    "gene": "TEKT3",
    "ensembl_id": "ENSG00000125409",
    "uniprot_id": "Q9BXF9",
    "tissue": "skin cancer",
    "cell_type": "Tumor cells",
    "patient_id": 3354,
    "patient_age": 92,
    "patient_sex": "male",
    "snomed_code": "M-80703;T-01000",
    "snomed_text": "Squamous cell carcinoma, NOS;Skin",
    "staining_intensity": "negative",
    "staining_location": "none",
    "staining_quantity": "none",
    "generic_caption": "Immunohistochemical staining of human skin cancer...",
    "caption_1": "Detailed caption describing the image...",
    "caption_2": "Alternative caption...",
    "url": "http://images.proteinatlas.org/...",
    "bboxes": [[x, y, w, h], ...],
    "rle_mask": "encoded_segmentation_mask",
    "area_px": 3883806,
    "area_fraction": 0.431534
  }
}

Index Files (Feather Format)

The hpa10m_tar_summary/all.feather file contains an index of all images with columns:

Column	Description
tar_filename	Source tar archive name
split	Dataset split (train/validation)
name	Full path within tar archive
type	Image type (pathology/tissue)
img_offset	Byte offset of image in tar
img_size	Image file size in bytes
json_offset	Byte offset of JSON in tar
json_size	JSON file size in bytes

Key Annotations

Clinical Information

• gene: Gene name (e.g., "TEKT3")
• ensembl_id: Ensembl gene ID (e.g., "ENSG00000125409")
• uniprot_id: UniProt protein ID (e.g., "Q9BXF9")
• tissue: Tissue or cancer type (e.g., "skin cancer")
• uberon_id: UBERON ontology ID
• cell_type: Cell type (e.g., "Tumor cells")
• patient_id: Patient identifier
• patient_age: Patient age
• patient_sex: Patient sex ("male" / "female")
• snomed_code: SNOMED-CT code (e.g., "M-80703;T-01000")
• snomed_text: SNOMED-CT description (e.g., "Squamous cell carcinoma, NOS;Skin")

Staining Characteristics

• staining_intensity: "negative", "weak", "moderate", "strong"
• staining_location: "nuclear", "cytoplasmic/membranous", "cytoplasmic/membranous,nuclear", "none"
• staining_quantity: "none", "<25%", "25-75%", ">75%"

Segmentation Data

• bboxes: Bounding boxes in [[x, y, width, height], ...] format
• rle_mask: Segmentation mask
• area_px: Segmented area in pixels
• area_fraction: Fraction of image covered by segmentation

Natural Language Captions

• generic_caption: Standardized description
• caption_1: Detailed scientific description
• caption_2: Alternative description

Other Metadata

• url: Original image URL from Human Protein Atlas
• image_md5: MD5 hash of original image
• file_size_kb: Image file size in KB

Usage

Loading Index with Pandas

import pandas as pd

# Load complete index
df = pd.read_feather("hpa10m_tar_summary/all.feather")

# Filter by split
train_df = df[df["split"] == "train"]
val_df = df[df["split"] == "validation"]

# Filter by image type
pathology_df = df[df["type"] == "pathology"]
tissue_df = df[df["type"] == "tissue"]

Data Source

This dataset is derived from the Human Protein Atlas (https://www.proteinatlas.org/), a comprehensive resource for protein expression in human tissues and cancers.

License

Please refer to the Human Protein Atlas data usage terms at https://www.proteinatlas.org/about/licence for licensing information.

📧 Contact

For questions or suggestions, please contact: jjnirschl@wisc.edu or zhi.huang@pennmedicine.upenn.edu