MorphPT classifies cell types from DAPI-stained nuclei. Each cell is described by two co-registered grayscale crops — a 2.5× view (fine nuclear morphology) and a 10× view (broader tissue context) — encoded by a shared DINOv3 ViT-B backbone with LoRA adaptation and fused by a learnable ScaleGate module. A coarse router predicts one of six broad morphology-informed groups, and group experts resolve the fine cell type via soft routing.

Here 2.5× and 10× denote crop field-of-view scales (how much area around the cell each crop spans), not microscope objective magnification: the 2.5× crop is a tight view of the nucleus, while the 10× crop covers more surrounding tissue.

💻 Code: github.com/AnitaCao/MorphPT
🗂️ CellImageNet benchmark dataset: huggingface.co/datasets/jilab/CellImageNet
🤖 MorphAgent (NL controller): github.com/ZBox1005/MorphAgent
📄 Paper: "A visual foundation model for cell classification" — citation forthcoming

Files

README.md                       # this card
LICENSE.md                      # DINOv3 License (required)
FINE_LABELS.md                  # fine cell types per coarse group (reference)
router/best.pt                  router/coarse_to_id.json     # 6-way coarse router
expert_Cancer/best.pt           expert_Cancer/class_to_idx.json
expert_Lymphoid/best.pt         expert_Lymphoid/class_to_idx.json
expert_Neuroglial/best.pt       expert_Neuroglial/class_to_idx.json
expert_Tissue_Vascular/best.pt  expert_Tissue_Vascular/class_to_idx.json
splits/                         # split spec + label maps (reproducibility)

Each checkpoint is self-contained and includes model weights, args, and label metadata needed for inference. The architecture is reconstructed from args at load time. The frozen DINOv3 ViT-B/16 backbone weights are embedded in each checkpoint.

Label space

The released model covers 21 fine cell types grouped into 6 broad morphology-informed groups (a human-tissue subset of CellImageNet):

Group	Type of stage	Fine cell types
Cancer	expert	Colon / Liver / Lung / Ovary / Pancreas / Skin cancer cells
Lymphoid	expert	B cells, NK cells, T cells
Neuroglial	expert	Astrocytes, Microglia, Neurons, Oligodendrocytes
Tissue_Vascular	expert	Endothelial, Epithelial, Fibroblasts, Myeloid, Pericytes, Smooth muscle cells
Stromal	passthrough	Stromal cells
Stem_Progenitor	passthrough	Stem and progenitor cells

Single-type groups (Stromal, Stem_Progenitor) are passthrough: the router label maps directly to the cell type with no expert.

Usage

Install the MorphPT package, then load the model and predict:

pip install git+https://github.com/AnitaCao/MorphPT.git

python

from morphpt import MorphPTPredictor

model = MorphPTPredictor.from_pretrained("jilab/MorphPT")   # downloads the weights
out = model.predict_one("cell_2p5x.png", "cell_10x.png")
print(out["pred"], out["coarse_group"], round(out["confidence"], 3))

Or download the weights and run the example CLI:

bash

hf download jilab/MorphPT --repo-type model --local-dir morphpt_weights
python examples/predict_cell.py --weights_dir morphpt_weights \
    --img_2p5x cell_2p5x.png --img_10x cell_10x.png

Inputs are paired grayscale DAPI crops of the same cell. Crops are resized to 224×224 and ImageNet-normalized internally.

Prediction uses soft routing. For multi-type groups, expert probabilities are weighted by the router probability for that group. For passthrough groups, the router probability is assigned directly to the corresponding fine label. The final cell type is chosen by the highest fine-type score:

s(y) = Σ_{g in experts} q(g) p_g(y) + Σ_{g in passthrough} q(g) 1[y = y_g]

Training data

Trained on a human-tissue subset of CellImageNet, subsampled per class to mitigate class imbalance. CellImageNet is a large-scale single-cell DAPI image database (~10M cells; 28 human + 14 mouse tissues) built from public 10x Genomics Xenium data; the full dataset is released separately as a benchmark at https://huggingface.co/datasets/jilab/CellImageNet. The split specification used to train this model (seed, per-class subsample targets, and label maps) is provided in this repository under splits/ for reproducibility. This checkpoint does not cover mouse tissues or cell types outside the 21 listed above.

Intended use and limitations

Intended use. MorphPT is intended for research use in fine-grained cell-type classification from DAPI nuclear-morphology crops in human tissue, including Xenium-style single-cell imagery. It may also be useful as a morphology-pretrained backbone for related research tasks.

Not for clinical or diagnostic use. This model is a research artifact, not a medical device, and should not be used for clinical diagnosis, treatment decisions, or other patient-care decisions.

Scope and limitations.

Human tissue and DAPI input only. Mouse tissue, other stains, and other imaging modalities are unvalidated.
Inputs must be correctly framed 2.5× and 10× crops of the same cell. Resizing fixes pixel size, not field of view. Mismatched crop scales may silently degrade predictions.
The model was trained on a per-class-subsampled subset of CellImageNet. Rare cell types and out-of-distribution tissues may have weaker performance.
Coarse routing is the main error source. Cells near coarse-group boundaries are likely to be less reliable.

Related projects

MorphAgent is a natural-language controller that orchestrates cell segmentation, MorphPT-based cell-type classification, and tumor–immune spatial analysis through an interactive web interface — github.com/ZBox1005/MorphAgent (MIT License). MorphAgent trains no models of its own: it delegates cell-type prediction to MorphPT and performs planning with a general-purpose LLM accessed through a provider API (e.g. OpenAI GPT-4o or Anthropic Claude), so no model weights are distributed with it. A versioned archival snapshot will be deposited on Zenodo upon publication.

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}
}

This work builds on DINOv3 (Meta AI) and the timm library; please also cite DINOv3 and timm (Ross Wightman, PyTorch Image Models).

License

This model is a derivative work of Meta's DINOv3 — the DINOv3 ViT-B backbone weights are embedded in every checkpoint — and is therefore distributed under the DINOv3 License, not a standard permissive license. A copy of the DINOv3 License is included in this repository as LICENSE.md, copied from the official facebookresearch/dinov3 repository. Authoritative page: https://ai.meta.com/resources/models-and-libraries/dinov3-license/

By using these weights you agree to the DINOv3 License terms, including:

redistribution of the weights or any derivative works must be under the DINOv3 License and must include a copy of it;
products, websites, or publications built on this model must display "Built with DINOv3" and acknowledge the use of DINOv3;
DINOv3's prohibited-use and export-control terms apply.

Components authored by the MorphPT team (LoRA adapters, ScaleGate, classification heads, label maps, and inference code) are released for research use consistent with the inherited DINOv3 License above.