metadata
license: mit
tags:
- spatial-transcriptomics
- computational-pathology
- histology
- gene-expression-prediction
- uncertainty-quantification
pipeline_tag: image-to-image
DELPHI
Pre-trained weights for the DELPHI model described in:
DELPHI: Dual-scale calibrated confidence enables trustworthy spatial gene expression prediction from histology. Under review.
Model
| File | Size | Description |
|---|---|---|
delphi_her2st.pt |
34 MB | HER2ST full-dataset model (seed 42, epoch 22) |
Usage
from src.model import DELPHI
import torch
model = DELPHI(uni2h_dim=1536, hidden_dim=384, num_genes=785,
gh=12, gw=12, knn_k=8, n_swin_blocks=4)
model.load_state_dict(torch.load("delphi_her2st.pt", map_location="cpu"))
model.eval()
# Inference: x [N, 1536] UNI2-h features, pos [N, 2] coordinates
with torch.no_grad():
mu, log_phi, pi, sigma_al, sigma_ep = model(x, pos)
Architecture
Frozen UNI2-h ViT encoder → 12×12 Swin grid (K=8) → Bayesian Last Layer → Hurdle-Gaussian head
Citation
@article{delphi2025,
title={DELPHI: Dual-scale calibrated confidence enables trustworthy
spatial gene expression prediction from histology},
author={...},
journal={Under review},
year={2025}
}