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ImmunoInstruction

Tasks:
Visual Question Answering
Image-to-Text
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Languages:
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pathology
histopathology
immunohistochemistry
computational-pathology
medical-imaging
vision-language
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metadata 
license: other
license_name: immunoinstruction-research-use
license_link: LICENSE
task_categories:
  - visual-question-answering
  - image-to-text
language:
  - en
tags:
  - pathology
  - histopathology
  - immunohistochemistry
  - computational-pathology
  - medical-imaging
  - vision-language
  - VQA
  - stain-transfer
pretty_name: ImmunoInstruction
size_categories:
  - 10K<n<100K
extra_gated_prompt: >-
  ## Terms of Access

  ImmunoInstruction is released for **non-commercial academic research purposes
  only**. By requesting access, you agree that:

  - You will use this dataset solely for research and educational purposes. -
  You will not attempt to re-identify any individual or institution
    associated with the underlying histopathology slides.
  - You will not redistribute the dataset, in whole or in part, without
    permission.
  - Any publication or derivative work using this dataset will cite the
    original paper (see Citation section).

  Please fill in the information below accurately. Access requests are reviewed
  manually.
extra_gated_fields:
  Full name: text
  Country: country
  Institution/Affiliation: text
  Email: text
  Intended use of this dataset: text
  I agree to use this dataset for non-commercial research purposes only: checkbox
extra_gated_button_content: Submit access request
extra_gated_heading: Request access to ImmunoInstruction

ImmunoInstruction

ImmunoInstruction is a large-scale IHC-positive-expression (IPE) instruction-following visual question answering (VQA) dataset for computational pathology. It was constructed to train VLEGA (VL Expert-Guided Assessment model), a lightweight vision-language model that emulates pathologist reasoning to assess the quality of generated immunohistochemistry (IHC) images. VLEGA is the core evaluator behind MEGFS (Multimodal Expert-Guided Finer Selection), a key component of DMCoStain, an iterative data-model co-optimization framework for H&E-to-IHC stain transfer.

📄 Paper: Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment (ACM Multimedia 2026) 💻 Code: https://github.com/SikangSHU/DMCoStain

Dataset Summary

Images were collected from the training splits of two public H&E–IHC paired datasets, MIST and HIT, spanning two tissue types and six biomarkers:

Subset Tissue Biomarker Staining Location # IHC Images
MIST-ER Human breast cancer ER Nuclear 4,093
MIST-PR Human breast cancer PR Nuclear 4,134
MIST-Ki67 Human breast cancer Ki67 Nuclear 4,334
MIST-HER2 Human breast cancer HER2 Membranous 4,642
HIT-PAX5 Canine lymphoma PAX5 Nuclear 6,043
HIT-CD3 Canine lymphoma CD3 Membranous 6,228
Total 29,474

Each IHC image (1024×1024 resolution) is paired with 4 question–answer pairs, spanning four clinically grounded assessment categories defined by pathologists:

  1. Style Fidelity (C1) — Whether the overall color, texture, and appearance of the image is consistent with real IHC images.
  2. Marker Location (C2) — Which cellular compartment (membrane, cytoplasm, or nucleus) the positive signal is localized to.
  3. Marker Proportion & Spatial Position (C3) — The approximate percentage of the image covered by the positive signal and its spatial distribution.
  4. Marker Intensity (C4) — The strength of the positive signal (no signal / weak / moderate / strong).

Note on this release: In the original paper, each image is additionally paired with a contrastive "Style Fidelity" negative question (C1-Q2), which uses out-of-domain noisy images (H&E, immunofluorescence, or heavily blurred IHC) to teach the model to reject non-IHC-style inputs. These noisy negative images are not included in this release, so each image here retains 4 QA pairs (rather than 5) covering the four core categories above.

All initial answers were generated by GPT-4o following predefined instructions, then reviewed and refined by four pathology experts to ensure clinical accuracy.

Dataset Structure 

├── stage2_MIST_ER_png/       # IHC images (ER)
├── stage2_MIST_ER.json       # QA pairs (ER)
├── stage2_MIST_PR_png/
├── stage2_MIST_PR.json
├── stage2_MIST_Ki67_png/
├── stage2_MIST_Ki67.json
├── stage2_MIST_HER2_png/
├── stage2_MIST_HER2.json
├── stage2_HIT_PAX5_png/
├── stage2_HIT_PAX5.json
├── stage2_HIT_CD3_png/
└── stage2_HIT_CD3.json

Each <subset>.json file contains the QA annotations for the corresponding image folder, with each entry linking an image filename to its four question–answer pairs (Style Fidelity, Marker Location, Marker Proportion & Spatial Position, Marker Intensity). The <subset>_csv/ folders provide the same annotations in tabular form.

Data Splits

Following the original paper: for MIST-ER, MIST-PR, MIST-Ki67 (nuclear biomarkers) and HIT-CD3 (membranous), 80% of images are used for training and 20% for testing. MIST-HER2 (membranous) and HIT-PAX5 (nuclear) are held out entirely as external test sets to evaluate generalization to biomarkers unseen during training. Stratified sampling was used to preserve the natural (imbalanced) answer distribution within each biomarker.

Intended Use

This dataset is intended for training and evaluating vision-language models on immunohistochemistry-positive-expression (IPE) assessment tasks — e.g., automated quality control of virtually generated (stain-transferred) IHC images, or general IHC image understanding. It underlies the VLEGA model used in the MEGFS/DMCoStain pipeline.

Citation

If you use this dataset, please cite:

@article{xu2026towards,
  title={Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment},
  author={Xu, Siyuan and Wang, Yan and Song, Haofei and Gao, Lili and Wang, Jiansheng and Zhang, Qing and Huang, Dan and Yun, Boxiang and Xiong, Hongkai and Li, Qingli},
  journal={arXiv preprint arXiv:2607.25393},
  year={2026}
}

Source Data

Images are derived from the publicly released MIST [1] and HIT [2] datasets. Please also respect the original terms of use of MIST and HIT when using the underlying images.

[1] Li F, et al. Adaptive supervised patchnce loss for learning h&e-to-ihc stain translation with inconsistent groundtruth image pairs. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2023: 632-641.

[2] Zhang W, et al. High-resolution medical image translation via patch alignment-based bidirectional contrastive learning. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2024: 178-188.

Contact

For questions about this dataset, please open a discussion on this Hugging Face repository or contact the first author listed in the paper.