Transfer Survival Forest (TSF)

📌 Project Overview

Transfer Survival Forest (TSF) is a method designed for small-sample survival analysis. It is based on Random Survival Forest (RSF) and utilizes transfer learning to improve survival prediction accuracy. This project implements TSF training, feature probability calculation, and target forest fine-tuning.

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📂 Code Structure

TSF/
│── rsf_models/                # Directory to store trained RSF models
│── data/                      # Directory to store datasets (SEER as an example)
│   ├── SEER.csv               # Pretrain data example
│── source_forest.py           # Trains the source forest and saves the model
│── target_forest_finetune.py  # Transfers and fine-tunes the target forest
│── dp_based.py                # DP-based target forest training
│── calculate_dp.py            # Computes feature probabilities
│── model/
│   ├── TransferSurvivalForest.py  # Core TSF implementation
│   ├── TransferTree.py  # Core TSF implementation
│   ├── methods.py                 # Preprocessing and utility functions
│── global_names.py               # Global variables
│── README.md                     # Project documentation

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🚀 Installation & Setup

1️⃣ Install Dependencies

Run the following command to install necessary Python libraries:

pip install -r requirements.txt

2️⃣ Data Preparation

• The pretrained dataset and target dataset must be aligned (i.e., they must have the same number of features and feature names).
• The repository provides a pretrain data sample (data/SEER.csv)
• Please put your training data in the folder data.

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🎯 Usage

1️⃣ Train Source Forest

python train_source_forest.py

• This script:
  • Loads the WCH dataset.
  • Trains the Random Survival Forest (RSF) (Source Forest).
  • Saves the trained model in rsf_models/source_forest.pkl.
  • Computes feature probability and saves it in dp.csv.

2️⃣ Transfer & Fine-tune Target Forest

python target_forest_finetune.py

• This script:
  • Loads the pretrained source_forest.pkl.
  • Fine-tunes the model on the target dataset.
  • Uses 10-fold cross-validation for evaluation.
  • Outputs the Concordance Index (CTD) score.

3️⃣ DP-Based Target Forest Training

python dp_based.py

• This script:
  • Computes and applies Depth Probability (DP) method.
  • Trains the RSF with transferred structures.
  • Evaluates the model using 10-fold cross-validation and computes CTD scores.

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📝 Future Improvements

• Upload public datasets.
• Integration of deep learning transfer methods (e.g., DeepSurv, DeepHit).

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🔥 Citation

If you use this code in your research, please cite the following paper: [Zhao, Y., Li, C., Shu, C., Wu, Q., Li, H., Xu, C., Li, T., Wang, Z., Luo, Z., & He, Y. (2025). Tackling small sample survival analysis via transfer learning: A study of colorectal cancer prognosis. arXiv preprint arXiv:2501.12421. https://arxiv.org/abs/2501.12421]

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📩 Contact

For questions or suggestions, please reach out to:

• Author: Yonghao Zhao
• Email: [email protected]