| --- |
| 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 |
|
|
| ```python |
| 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 |
|
|
| ```bibtex |
| @article{delphi2025, |
| title={DELPHI: Dual-scale calibrated confidence enables trustworthy |
| spatial gene expression prediction from histology}, |
| author={...}, |
| journal={Under review}, |
| year={2025} |
| } |
| ``` |
|
|
