3. TeCh: a unified CoTAR-based framework that captures temporal, channel, or both via adaptive tokenization.
- Install requirements.
pip install -r requirements.txt
- Prepare data. You can download all datasets from Medformer. All the datasets are well pre-processed (except for the TDBrain dataset, which requires permission first) and can be used easily thanks to their efforts. Then, place all datasets under the folder
./dataset
- Train the model. We provide the experiment scripts of all benchmarks under the folder
./scripts
- For example, you can use the command line below to get the result of APAVA. The whole training history is under the './logs' folder.
bash ./scripts/APAVA.sh
If you find this repo helpful, please cite our paper.
@inproceedings{
yu2026tech,
title={Decentralized Attention Fails Centralized Signals: Rethinking Transformers for Medical Time Series},
author={Guoqi Yu and Juncheng Wang and Chen Yang and Jing Qin and Angelica I Aviles-Rivero and Shujun Wang},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=oZJFY2BQt2}
}
This project is built on the code in the repo Medformer. Thanks a lot for their amazing work!
Please also star their project and cite their paper if you find this repo useful.
@article{wang2024medformer,
title={Medformer: A multi-granularity patching transformer for medical time-series classification},
author={Wang, Yihe and Huang, Nan and Li, Taida and Yan, Yujun and Zhang, Xiang},
journal={Advances in Neural Information Processing Systems},
volume={37},
pages={36314--36341},
year={2024}
}