miniMTI-CRC-example / README.md
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metadata
license: other
license_name: ohsu-non-commercial
license_link: https://github.com/ChangLab/miniMTI/blob/publication/LICENSE
tags:
  - biology
  - multiplex-imaging
  - computational-pathology
  - cycif
  - colorectal-cancer
size_categories:
  - 1K<n<10K

miniMTI-CRC Example Data

Example single-cell imaging data for testing miniMTI, a molecularly anchored virtual staining framework for multiplex tissue imaging panel reduction.

Paper: bioRxiv 2026.01.21.700911
Code: GitHub
Model: changlab/miniMTI-CRC

Dataset Description

10,000 single-cell image patches randomly sampled (seed=42) from CRC-Orion sample CRC04 (colorectal cancer tissue WSI, RareCyte Orion platform).

File

  • example_CRC04_10k.h5 — HDF5 file (~178 MB)

HDF5 Structure

Dataset Shape Type Description
images (10000, 32, 32, 20) uint8 17 IF channels + 3 H&E (RGB) channels
masks (10000, 32, 32) bool Binary cell segmentation masks
metadata (10000,) string Cell IDs and coordinates: <sample>-CellID-<id>-x=<x>-y=<y>

Channel Ordering (20 raw channels)

Index Channel
0 DAPI
1 CD31
2 CD45
3 CD68
4 CD4
5 FOXP3
6 CD8a
7 CD45RO
8 CD20
9 PD-L1
10 CD3e
11 CD163
12 E-cadherin
13 PD-1
14 Ki67
15 PanCK
16 aSMA
17 H&E (R)
18 H&E (G)
19 H&E (B)

Channels 0–16 are immunofluorescence markers. Channels 17–19 are co-registered H&E RGB. The miniMTI model treats each IF channel as a separate marker and the three H&E channels as a single marker (18 markers total).

Usage

from huggingface_hub import hf_hub_download

# Download example data
path = hf_hub_download(
    repo_id="changlab/miniMTI-CRC-example",
    filename="example_CRC04_10k.h5",
    repo_type="dataset",
)
# Run inference with miniMTI
python scripts/inference_example.py \
    --val-file $path \
    --input-channels 17,6,11,13

Citation

@article{sims2026minimti,
  title={miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology},
  author={Sims, Z. and Govindarajan, S. and Ait-Ahmad, K. and Ak, C. and Kuykendall, M. and Mills, G. B. and Eksi, E. and Chang, Y. H.},
  journal={bioRxiv},
  year={2026},
  doi={10.64898/2026.01.21.700911}
}