You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

These weights are released under CC BY-NC 4.0 — strictly non-commercial,
research and educational use only. By requesting access you agree to:

Use the weights only for non-commercial research.
Cite the SPARC paper in any derived publication.
Not redistribute the weights to third parties.

SPARC

Gene-program-aware survival modelling from H&E whole-slide images.

This repository hosts the trained model weights for the SPARC paper (Ayed, Cohn, et al.). Code, configs, training scripts, and figure-regeneration notebooks live at github.com/aziz-ayed/SPARC.

SPARC is a two-stage pipeline:

SPARC-Map predicts 40 hallmark gene-expression-program (GEP) scores per H&E patch, recovering a spatial molecular map of each slide.
SPARC-Risk fuses those per-patch GEP scores with the same H&E features through a signature-query attention head and a cancer-aware gate, producing a single per-patient risk score.

These weights cover the SPARC-Risk model, the image-only baseline used for ablations, and the SPARC-Map GEP-prediction model.

SPARC pipeline

What you get

SPARC-Risk

Folder	Model	Description
`sparc_risk/`	SPARC-Risk (canonical)	Signature-query fusion + H&E. The model reported throughout the paper.
`image_only/`	Image-only baseline	Same backbone, GEP pathway disabled. Use for direct ablation against SPARC-Risk.

Each folder contains 5 checkpoints — fold_0_best.pt through fold_4_best.pt — corresponding to the 5-fold cross-validation splits described in the paper and in data/mmp_hybrid_splits_v2_20cancer.csv.

Every .pt carries both model_state_dict and the original training config, so the model can be rebuilt with one line:

import torch
from sparc.models.factory import build_model

ckpt = torch.load("sparc_risk/fold_0_best.pt", map_location="cpu", weights_only=False)
model = build_model(ckpt["config"])
model.load_state_dict(ckpt["model_state_dict"])
model.eval()

SPARC-Map

Folder	Model	Description
`sparc_map/`	SPARC-Map	kNN-transformer that predicts 40 hallmark GEP scores per H&E patch from the same H-optimus-1 features used by SPARC-Risk.

Unlike SPARC-Risk, this ships as a single checkpoint — knn_transformer_robust_normalized_mse_pearson.pth — rather than a 5-fold ensemble.

The checkpoint carries the training args (hyperparameters), the model_state_dict, per-program normalisation stats (norm_stats), and the ordered list of 40 GEP program names (program_names) the model was trained to predict:

import torch

ckpt = torch.load(
    "sparc_map/knn_transformer_robust_normalized_mse_pearson.pth",
    map_location="cpu", weights_only=False,
)
args = ckpt["args"]                    # hyperparameters used to build the model
program_names = ckpt["program_names"]  # the 40 GEP programs, in output order

# Build the kNN-transformer (see the training script in the SPARC repo) with
# args["hidden_dim"], args["num_layers"], args["num_heads"], args["k_neighbors"],
# then: model.load_state_dict(ckpt["model_state_dict"])

Quick start

# 1. Install the SPARC package
git clone https://github.com/aziz-ayed/SPARC.git && cd SPARC
conda env create -f environment.yml
conda activate sparc

# 2. Accept the license on https://huggingface.co/azizayed/SPARC, then:
pip install -U "huggingface_hub[cli]"
hf auth login
hf download azizayed/SPARC --local-dir checkpoints

# 3. Inference on an external cohort (e.g. NLST lung)
python -m inference.run \
    --cohort nlst \
    --checkpoint_dir checkpoints/sparc_risk \
    --gpus 0,1,2,3

The download produces:

checkpoints/
├── sparc_risk/   fold_{0..4}_best.pt
├── image_only/   fold_{0..4}_best.pt
└── sparc_map/    knn_transformer_robust_normalized_mse_pearson.pth

Architecture (SPARC-Risk)

Component	Setting
Image backbone	H-optimus-1 (1536-dim)
Patch size / magnification	224 px @ 20×
Max patches per slide	4096
Fusion	Signature-query cross-attention (64-NN, 4 heads)
Aggregator	Gated attention MIL
Head	Discrete-time NLL survival, 4 bins
Cancer conditioning	Per-cancer learned gate
Hidden dim	256
Trainable params	≈ 2.6 M
Optimiser / schedule	Adam, lr 1 × 10⁻⁴, cosine T_max 20
Random seed	1337

Full config + reproduction recipe: configs/sparc_risk.yaml.

Architecture (SPARC-Map)

Component	Setting
Image backbone	H-optimus-1 (1536-dim), same per-patch features as SPARC-Risk
Patch size / magnification	224 px @ 20×
Spatial context	k-NN patch graph, k = 128 neighbours; coordinate MLP encoder (2 → 64 → 256)
Encoder	3-layer pre-norm Transformer, 8 heads, hidden dim 256, MLP ratio 4×
Output head	256 → 128 → 40 (per-patch GEP program scores)
Programs predicted	40 hallmark / curated gene-expression programs
Label normalisation	Per-program
Loss	MSE + Pearson correlation (`mse+pearson`)
Trainable params	≈ 2.8 M
Optimiser	Adam, lr 1 × 10⁻⁴, weight decay 1 × 10⁻²
Batch size / epochs	32 / up to 50 (this checkpoint: epoch 38, val R² ≈ 0.66)

Training data

SPARC-Risk

5-fold patient-level cross-validation over 20 TCGA cancer types (BLCA, BRCA, CESC, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, PAAD, READ, SARC, SKCM, STAD, UCEC, plus a held-out evaluation split). Splits derive from the MMP hybrid scheme of Mahmood et al. and are released alongside the code at data/mmp_hybrid_splits_v2_20cancer.csv.

External validation cohorts (not used for training) — NLST lung, SurGen CRC, Yale breast, ovarian — are described in the paper.

SPARC-Map

Trained on HEST-1k (Jaume et al.), a public dataset of paired H&E whole-slide images and spatial transcriptomics spots spanning multiple organs and cancer types. Per-spot gene expression is summarised into the 40 hallmark/curated GEP scores used as regression targets, so SPARC-Map learns to predict spatially resolved GEP activity from H&E alone.

Intended use

These weights are intended for non-commercial biomedical research and education only. Acceptable uses include:

Reproducing the SPARC paper's results.
Benchmarking against SPARC-Risk in computational-pathology research.
Methodological extensions (new fusion designs, additional cohorts, ablation studies).

Citation

The SPARC paper is currently under review. Once a preprint or accepted version is available, a BibTeX entry will be added here. In the meantime, if you use these weights, please link back to github.com/aziz-ayed/SPARC and contact the corresponding author at azizayed@mit.edu.

License

These weights are released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). For commercial licensing, please contact the authors via the corresponding GitHub issues page.

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Paper for azizayed/SPARC

HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis

Paper • 2406.16192 • Published Jun 23, 2024 • 1