language:
- en
- zh
license: mit
library_name: pytorch
pipeline_tag: image-segmentation
tags:
- medical-imaging
- mri
- missing-modality
- brats
- isles
- wmh
ReLiSS
Inference, evaluation, completed-run logs and fixed data splits: hekaiwang/ReLiSS.
Training code and the complete experiment pipeline are not included in this release.
| Dataset | Protocol | Checkpoints | Channel order |
|---|---|---|---|
| BraTS2021 | Five folds, 1,251 subjects | fold 0–4, best | T1, T1ce, T2, FLAIR |
| ISLES2022 | Five folds, 250 subjects | fold 0–4, final | FLAIR, ADC, DWI |
| WMH2017 | Official 60 train / 110 test | fold all, final | FLAIR, T1 |
This repository contains eleven training checkpoints, matching plans, dataset metadata and splits. WMH uses the official train/test protocol; the training split file does not imply five released WMH models.
weights_manifest.json and SHA256SUMS record checkpoint sizes and SHA-256.
The checkpoint tensors and training state are preserved. The trainer
folder name is nnUNetTrainerReLiSS_300epochs; the supplied inference
script directly constructs the matching network and loads its state dictionary.
Download
From the installed code repository:
source scripts/env.sh
python scripts/download_weights.py --repo wanghekai/ReLiSS \
--revision COMMIT_HASH --output "$nnUNet_results"
Replace COMMIT_HASH with the immutable model revision in the code repository's
docs/WEIGHTS.md. The downloader fetches exactly eleven weights and verifies
sizes and hashes. Follow the code README for held-out inference and evaluation.
For cross-validation, use each subject's held-out fold. Missing channels are
zeroed after full-input preprocessing; the released protocol uses a shared crop.
Project code and model release are MIT licensed. Dataset and dependency terms remain separate. Obtain MRI data and reference masks from their authorized sources.