Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classification
Authors: Yihe Wang, Nan Huang, Taida Li, Yujun Yan, Xiang Zhang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on five public datasets under both subject-dependent and challenging subject-independent setups. Results demonstrate Medformer s superiority over 10 baselines, achieving top averaged ranking across five datasets on all six evaluation metrics. |
| Researcher Affiliation | Academia | Yihe Wang , Nan Huang , Taida Li University of North Carolina Charlotte {ywang145,nhuang1,tli14}@charlotte.edu Yujun Yan Dartmouth College yujun.yan@dartmouth.edu Xiang Zhang University of North Carolina Charlotte xiang.zhang@charlotte.edu |
| Pseudocode | No | The paper describes its method using natural language and figures, but does not include formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the source code at https://github.com/DL4m Health/Medformer. |
| Open Datasets | Yes | We conduct extensive experiments on five public datasets... Datasets. (1) APAVA [67] is an EEG dataset... (2) TDBRAIN [68] is an EEG dataset... (3) ADFTD [69, 19] is an EEG dataset... (4) PTB [70] is an ECG dataset... (5) PTB-XL [71] is an ECG dataset... URLs for these datasets are provided in Appendix B: https://osf.io/jbysn/ for APAVA, https://brainclinics.com/resources/ for TDBrain, https://openneuro.org/datasets/ds004504/versions/1.0.6 for ADFTD, https://physionet.org/content/ptbdb/1.0.0/ for PTB, https://physionet.org/content/ptb-xl/1.0.3/ for PTB-XL. |
| Dataset Splits | Yes | In this setup [Subject-Dependent], the division into training, validation, and test sets is based on time series samples. In this setup [Subject-Independent], the division into training, validation, and test sets is based on subjects. For the training, validation, and test set splits, we employ the subject-independent setup. Samples with subject IDs {15,16,19,20} and {1,2,17,18} are assigned to the validation and test sets, respectively. The remaining samples are allocated to the training set. |
| Hardware Specification | Yes | All experiments are run on an NVIDIA RTX 4090 GPU and a server with 4 RTX A5000 GPUs. |
| Software Dependencies | No | The paper mentions software like 'Time-Series-Library project', 'Py Torch', and 'Adam optimizer', but it does not specify version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | For all methods, we employ 6 layers for the encoder, with the self-attention dimension D set to 128 and the hidden dimension of the feed-forward networks set to 256. The optimizer used is Adam, with a learning rate of 1e-4. The batch size is set to {32,32,128,128,128} for the datasets APAVA, TDBrain, ADFD, PTB, and PTB-XL, respectively. Training is conducted for 100 epochs, with early stopping triggered after 10 epochs without improvement in the F1 score on the validation set. |