HAPPY — trained model weights

Trained weights for the HAPPY placenta histology pipeline (Histology Analysis Pipeline.PY): a three-stage workflow of nuclei detection → cell classification → tissue classification over whole slide images.

• Code: https://github.com/Nellaker-group/happy
• Original methods paper: Vanea et al., Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY, Nature Communications (2024).
• HAPPY v2/v3: Walker et al., Biologically inspired digital histology for deep phenotyping of placental composition changes across major lesion types, Placenta (2026).

These weights are used by the HAPPY code.

Files

File	Stage	Architecture	Coverage	Metric
nuclei/retinanet_placenta_nuclei_detector.pt	Nuclei detection	RetinaNet	Placenta	F1 0.856
nuclei/yolo_multiorgan_nuclei_detector.pt	Nuclei detection	YOLO (yolo26)	Multi-organ	F1 0.90
cell/resnet50_placenta_cell_classifier.pt	Cell classification	ResNet-50	Placenta (11 cell types)	Acc 84.29%
tissue/clustergcn_placenta_tissue_classifier.pt	Tissue classification	ClusterGCN (GNN)	Placenta (9 tissue types)	Acc 68.34%

Nuclei detector — per-organ benchmarks (YOLO, multi-organ)

Organ	F1	Precision	Recall
Aorta	0.923	0.870	0.983
Brain cortex	0.955	0.913	1.000
Cervix ectocervix	0.815	0.692	0.991
Liver	0.908	0.885	0.932
Muscle skeletal	0.931	0.871	1.000
Ovary	0.852	0.755	0.978
Placenta	0.899	0.857	0.946

The RetinaNet detector is placenta-only (F1 0.856) and kept as an alternative to the multi-organ YOLO detector.

Cell classification (placenta, 11 cell types)

Evaluated on a held-out test set of 2,743 cells:

Metric	Value
Overall accuracy	84.29%
Top-2 accuracy	94.90%
Macro-averaged ROC AUC	0.9773

Most misclassifications fall within closely related cell differentiation pathways.

Tissue classification (placenta, 9 tissue types)

Evaluated on 149,425 cell-graph nodes:

Metric	Value
Overall accuracy	68.34%
Top-2 accuracy	91.14%
Top-3 accuracy	97.10%
Macro-averaged ROC AUC	0.8868

Misclassifications fall primarily within developmentally similar microstructures — villus types are typically confused with other villus types, reflecting similarities in villus growth and branching morphology.

Training & evaluation data

Model	Train	Validation	Test (held-out)
Nuclei detection (multi-organ YOLO)	25,990 nuclei	6,746 nuclei	7,777 nuclei
Nuclei detection (placenta RetinaNet)	11,755 nuclei	2,374 nuclei	2,754 nuclei
Cell classification	13,842 cells (incl. validation)	—	2,743 cells
Tissue classification (GNN)	468,869 nodes (incl. validation)	—	179,095 nodes

Usage

pip install huggingface_hub
# from the HAPPY repo:
python -m happy.db.download_models --project-name placenta

This downloads all four files into projects/placenta/trained_models/. They then resolve by id via main.db database:

# nuclei + cell inference, then tissue inference
python cell_inference.py  --project-name placenta --organ-name placenta \
    --nuc-model-id 2 --cell-model-id 3 --run-id <run>
python tissue_inference.py --project-name placenta --organ-name placenta \
    --tissue-model-id 1 --run-id <run>

Intended use & limitations

• Intended use: research on H&E histology WSIs, trained for placenta. The YOLO nuclei detector generalises across the organs benchmarked above.
• Validate before use: cell and tissue models are placenta-trained; on a new cohort of data, validate on a small amount of your own ground truth (see the repo's evaluation scripts) before relying on predictions.
• Not for clinical use. Research only.

Citation

If you use HAPPY or these models, please cite the original methods paper (Vanea et al. 2024). If you use these model weights or the v2/v3 pipeline, please also cite Walker et al. 2026.

@article{vanea2024happy,
  title   = {Mapping cell-to-tissue graphs across human placenta histology whole slide
             images using deep learning with HAPPY},
  author  = {Vanea, Claudia and D{\v{z}}igurski, Jelisaveta and Rukins, Valentina and Dodi,
             Omri and Siigur, Siim and Salum{\"a}e, Liis and Meir, Karen and Parks, W. Tony
             and Hochner-Celnikier, Drorith and Fraser, Abigail and Hochner, Hagit and Laisk,
             Triin and Ernst, Linda M. and Lindgren, Cecilia M. and Nell{\aa}ker, Christoffer},
  journal = {Nature Communications},
  volume  = {15},
  number  = {1},
  pages   = {2710},
  year    = {2024}
}

@article{walker2026happy,
  title   = {Biologically inspired digital histology for deep phenotyping of placental
             composition changes across major lesion types},
  author  = {Walker, Emma C. and Vanea, Claudia and Meir, Karen and Hochner-Celnikier,
             Drorith and Hochner, Hagit and Laisk, Triin and Lindgren, Cecilia and
             Glastonbury, Craig A. and Ernst, Linda M. and Nellaker, Christoffer},
  journal = {Placenta},
  year    = {2026}
}