CROPKT: Cross-Cancer Knowledge Transfer in WSI-based Prognosis Prediction

[Paper] | [UNI2-h-DSS Dataset] | [Zhihu (Chinese)] | [Acknowledgements] | [Citation]

Abstract: Whole-Slide Image (WSI) is an important tool for estimating cancer prognosis. Current studies generally follow a conventional cancer-specific paradigm where one cancer corresponds to one model. However, it naturally struggles to scale to rare tumors and cannot utilize the knowledge of other cancers. Although a multi-task learning-like framework has been studied recently, it usually has high demands on computational resources and needs considerable costs in iterative training on ultra-large multi-cancer WSI datasets. To this end, this paper makes a paradigm shift to knowledge transfer and presents the first preliminary yet systematic study on cross-cancer prognosis knowledge transfer in WSIs, called CROPKT. It has three major parts: (i) we curate a large dataset (UNI2-h-DSS) with 26 cancers and use it to measure the transferability of WSI-based prognostic knowledge across different cancers (including rare tumors); (ii) beyond a simple evaluation merely for benchmark, we design a range of experiments to gain deeper insights into the underlying mechanism of transferability; (iii) we further show the utility of cross-cancer knowledge transfer, by proposing a routing-based baseline approach (ROUPKT) that could often efficiently utilize the knowledge transferred from off-the-shelf models of other cancers. We hope CROPKT could serve as an inception and lay the foundation for this nascent paradigm, i.e., WSI-based prognosis prediction with cross-cancer knowledge transfer.

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On updating. Stay tuned.

📚 Recent updates:

• 25/09/25: created a repo for CROPKT

👩‍💻 Running Code

Pre-requisites

All experiments are run on a machine with

• two NVIDIA GeForce RTX 3090 GPUs
• python 3.8 and pytorch==1.11.0+cu113

Detailed package requirements:

• for pip or conda users, full requirements are provided in requirements.txt.
• for Docker users, you could use our base Docker image via docker pull yuukilp/deepath:py38-torch1.11.0-cuda11.3-cudnn8-devel and then install additional essential python packages (see requirements.txt) in the container.

Training Models

Use the following command to load a experimental configuration and then train & evaluate a survival model (based on 5-fold cross-validation):

python3 main.py --config config/cfg_sa_base_uni2h_stl_moe_pkt.yaml --handler SAT --multi_run

All important configurations are explained in config/cfg_sa_base_uni2h_stl_moe_pkt.yaml.

For training & evaluating traditional cancer-specific survival models, use the following:

python3 main.py --config config/cfg_sa_base_uni2h_stl.yaml --handler SA --multi_run

Training Logs

We advocate open-source research and would like to make our training logs publicly-available. Full training logs in this study can be accessed at Google Drive.

UNI2-h-DSS Dataset

HF Dataset (with complete DSS labels): https://huggingface.co/datasets/yuukilp/UNI2-h-DSS

Acknowledgements

We thank the following great works that contribute to this work:

• UNI: a state-of-the-art foundation model for pathology; it is used to extract patch features from WSIs.
• UNI2-h features: the datasets for this study are derived from it.
• TCGA GDC Data portal: it provides the source data for analysis.

📝 Citation

If you find this work helps your research, please consider citing our paper:

@misc{liu2025cropkt,
      title={Cross-Cancer Knowledge Transfer in WSI-based Prognosis Prediction}, 
      author={Pei Liu and Luping Ji and Jiaxiang Gou and Xiangxiang Zeng},
      year={2025},
      eprint={2508.13482},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2508.13482}, 
}