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PathLUPI

PyTorch
computational-pathology
whole-slide-image
molecular-prediction
survival-analysis
conch
pathlupi
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  1. README.md +150 -0
  2. subtyping/BLCA_FGFR3Sub/fold0.pth.tar +3 -0
  3. subtyping/BLCA_FGFR3Sub/fold1.pth.tar +3 -0
  4. subtyping/BLCA_FGFR3Sub/fold2.pth.tar +3 -0
  5. subtyping/BLCA_FGFR3Sub/fold3.pth.tar +3 -0
  6. subtyping/BLCA_FGFR3Sub/fold4.pth.tar +3 -0
  7. subtyping/BLCA_MolSub/fold0.pth.tar +3 -0
  8. subtyping/BLCA_MolSub/fold1.pth.tar +3 -0
  9. subtyping/BLCA_MolSub/fold2.pth.tar +3 -0
  10. subtyping/BLCA_MolSub/fold3.pth.tar +3 -0
  11. subtyping/BLCA_MolSub/fold4.pth.tar +3 -0
  12. subtyping/BRCA_TNBCSub/fold0.pth.tar +3 -0
  13. subtyping/BRCA_TNBCSub/fold1.pth.tar +3 -0
  14. subtyping/BRCA_TNBCSub/fold2.pth.tar +3 -0
  15. subtyping/BRCA_TNBCSub/fold3.pth.tar +3 -0
  16. subtyping/BRCA_TNBCSub/fold4.pth.tar +3 -0
  17. subtyping/CRC_BRAFSub/fold0.pth.tar +3 -0
  18. subtyping/CRC_BRAFSub/fold1.pth.tar +3 -0
  19. subtyping/CRC_BRAFSub/fold2.pth.tar +3 -0
  20. subtyping/CRC_BRAFSub/fold3.pth.tar +3 -0
  21. subtyping/CRC_BRAFSub/fold4.pth.tar +3 -0
  22. subtyping/CRC_TP53Sub/fold0.pth.tar +3 -0
  23. subtyping/CRC_TP53Sub/fold1.pth.tar +3 -0
  24. subtyping/CRC_TP53Sub/fold2.pth.tar +3 -0
  25. subtyping/CRC_TP53Sub/fold3.pth.tar +3 -0
  26. subtyping/CRC_TP53Sub/fold4.pth.tar +3 -0
  27. subtyping/LIHC_TP53Sub/fold0.pth.tar +3 -0
  28. subtyping/LIHC_TP53Sub/fold1.pth.tar +3 -0
  29. subtyping/LIHC_TP53Sub/fold2.pth.tar +3 -0
  30. subtyping/LIHC_TP53Sub/fold3.pth.tar +3 -0
  31. subtyping/LIHC_TP53Sub/fold4.pth.tar +3 -0
  32. subtyping/LUAD_EGFRSub/fold0.pth.tar +3 -0
  33. subtyping/LUAD_EGFRSub/fold1.pth.tar +3 -0
  34. subtyping/LUAD_EGFRSub/fold2.pth.tar +3 -0
  35. subtyping/LUAD_EGFRSub/fold3.pth.tar +3 -0
  36. subtyping/LUAD_EGFRSub/fold4.pth.tar +3 -0
  37. subtyping/LUAD_TP53Sub/fold0.pth.tar +3 -0
  38. subtyping/LUAD_TP53Sub/fold1.pth.tar +3 -0
  39. subtyping/LUAD_TP53Sub/fold2.pth.tar +3 -0
  40. subtyping/LUAD_TP53Sub/fold3.pth.tar +3 -0
  41. subtyping/LUAD_TP53Sub/fold4.pth.tar +3 -0
  42. subtyping/NSCLC_TMBSub/fold0.pth.tar +3 -0
  43. subtyping/NSCLC_TMBSub/fold1.pth.tar +3 -0
  44. subtyping/NSCLC_TMBSub/fold2.pth.tar +3 -0
  45. subtyping/NSCLC_TMBSub/fold3.pth.tar +3 -0
  46. subtyping/NSCLC_TMBSub/fold4.pth.tar +3 -0
  47. survival/GBM/fold2.pth.tar +3 -0
  48. survival/LUSC/fold0.pth.tar +3 -0
  49. survival/LUSC/fold3.pth.tar +3 -0
  50. survival/LUSC/fold4.pth.tar +3 -0
README.md ADDED
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+ ---
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+ license: cc-by-nc-nd-4.0
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+ library_name: pytorch
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+ tags:
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+ - computational-pathology
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+ - whole-slide-image
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+ - molecular-prediction
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+ - survival-analysis
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+ - conch
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+ - pathlupi
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+ ---
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+
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+ # PathLUPI Checkpoints
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+
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+ Official model checkpoints for **PathLUPI**, introduced in [Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images](https://arxiv.org/abs/2506.19681).
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+
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+ PathLUPI uses transcriptomic profiles as privileged information during training to learn genome-anchored representations from histology. At inference time, the model requires only WSI features extracted with [CONCH](https://github.com/mahmoodlab/CONCH); transcriptomic data are not required.
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+
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+ For model code, data preparation, and inference instructions, see the [PathLUPI GitHub repository](https://github.com/ChengJin-git/PathLUPI).
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+
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+ ## Available Checkpoints
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+
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+ This repository provides the five cross-validation folds for 37 TCGA-trained tasks reported in the paper:
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+
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+ - **24 biomarker and molecular prediction tasks**
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+ - **13 survival prognosis tasks**
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+ - **185 checkpoint files in total**
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+
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+ External cohorts in the paper were evaluated with the checkpoint for the corresponding TCGA-trained task and therefore do not require separate checkpoint files.
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+
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+ ### Biomarker Prediction
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+
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+ | Cancer type | Task |
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+ |---|---|
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+ | BLCA | FGFR3 mutation |
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+ | BRCA | ER, HER2, PIK3CA, PR, TNBC, TP53 |
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+ | CRC | BRAF, KRAS, TP53 |
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+ | GBMLGG | IDH1 mutation |
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+ | LIHC | TP53 mutation |
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+ | LUAD | EGFR, KRAS, TP53 |
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+ | NSCLC | Tumor mutation burden (TMB) |
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+ | SKCM | BRAF mutation |
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+
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+ ### Molecular Subtyping
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+
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+ | Directory | Cohort |
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+ |---|---|
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+ | `BLCA_MolSub` | Bladder cancer |
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+ | `BRCA_MolSub` | Breast cancer |
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+ | `CRC_MolSub` | Colorectal cancer |
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+ | `GBMLGG_MolSub` | Glioma |
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+ | `HNSC_MolSub` | Head and neck cancer |
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+ | `PanGI_MolSub` | Pan-gastrointestinal cancers |
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+ | `UCEC_MolSub` | Endometrial cancer |
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+
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+ ### Survival Prognosis
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+
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+ `BLCA`, `BRCA`, `CRC`, `GBM`, `HNSC`, `KIRC`, `LGG`, `LIHC`, `LUAD`, `LUSC`, `SKCM`, `STAD`, and `UCEC`.
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+
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+ GBM and LGG are provided as separate survival models, consistent with the final analysis in the paper.
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+
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+ ## Repository Structure
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+
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+ ```text
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+ PathLUPI/
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+ ├── subtyping/
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+ │ ├── BRCA_ERSub/
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+ │ │ ├── fold0.pth.tar
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+ │ │ ├── fold1.pth.tar
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+ │ │ ├── fold2.pth.tar
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+ │ │ ├── fold3.pth.tar
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+ │ │ └── fold4.pth.tar
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+ │ └── ...
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+ └── survival/
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+ ├── GBM/
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+ │ ├── fold0.pth.tar
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+ │ └── ...
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+ ├── LGG/
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+ │ ├── fold0.pth.tar
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+ │ └── ...
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+ └── ...
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+ ```
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+
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+ ## Download
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+
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+ Install the Hugging Face CLI:
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+
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+ ```bash
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+ pip install -U huggingface_hub
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+ ```
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+
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+ Download all checkpoints:
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+
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+ ```bash
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+ hf download peterjin0703/PathLUPI \
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+ --local-dir ./checkpoints/PathLUPI
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+ ```
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+
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+ Download one task only, for example BRCA ER prediction:
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+
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+ ```bash
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+ hf download peterjin0703/PathLUPI \
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+ --include "subtyping/BRCA_ERSub/*" \
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+ --local-dir ./checkpoints/PathLUPI
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+ ```
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+
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+ Download one survival model only:
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+
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+ ```bash
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+ hf download peterjin0703/PathLUPI \
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+ --include "survival/GBM/*" \
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+ --local-dir ./checkpoints/PathLUPI
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+ ```
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+
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+ ## Checkpoint Format
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+
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+ Each file is a PyTorch `state_dict` containing model weights only. Validation metrics, training epochs, optimizer states, and other training metadata are not stored in the checkpoint.
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+
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+ ```python
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+ import torch
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+
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+ state_dict = torch.load(
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+ "checkpoints/PathLUPI/subtyping/BRCA_ERSub/fold0.pth.tar",
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+ map_location="cpu",
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+ weights_only=True,
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+ )
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+ model.load_state_dict(state_dict)
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+ model.eval()
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+ ```
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+
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+ The model architecture and task-specific construction code are provided in the [GitHub repository](https://github.com/ChengJin-git/PathLUPI).
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+
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+ ## CONCH Dependency
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+
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+ These checkpoints operate on WSI features extracted using CONCH. The original CONCH weights are not redistributed here. Please obtain them directly from the [MahmoodLab CONCH model page](https://huggingface.co/MahmoodLab/CONCH) and follow its license and access requirements.
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{jin2025pathlupi,
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+ title={Genome-Anchored Foundation Model Embeddings Improve Molecular Prediction from Histology Images},
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+ author={Jin, Cheng and Zhou, Fengtao and Yu, Yunfang and Ma, Jiabo and Wang, Yihui and Xu, Yingxue and others},
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+ journal={arXiv preprint arXiv:2506.19681},
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+ year={2025}
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+ }
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+ ```
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+
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+ ## License
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+
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+ The PathLUPI checkpoints are released under the [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/) license. CONCH is distributed separately under its own terms.
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