| --- |
| license: cc-by-nc-4.0 |
| tags: |
| - medical-imaging |
| - missing-modality |
| - mri |
| - ct |
| --- |
| |
| # SphereFlow: Missing Modality Imputation via Geometric Transport on Hypersphere |
|
|
| Pretrained weights and preprocessed data for our NeurIPS 2026 paper. |
|
|
| [[Paper](https://arxiv.org/abs/2611.04414)] [[Code](https://github.com/WTHH031230/SphereFlow)] |
|
|
| ### Contents |
|
|
| ```text |
| checkpoints/ |
| ├── sphereflow/ |
| │ ├── brats/ |
| │ │ ├── rae.pt # shared DINOv2-B encoder + 4 ViT-G decoders |
| │ │ └── flow.pt # DiT^DH-XXL flow |
| │ └── mri_ct/ |
| │ ├── rae.pt # shared DINOv2-B encoder + 2 ViT-G decoders |
| │ └── flow.pt # DiT^DH-XL flow |
| ├── baselines/ |
| │ ├── brats/{MMSyn,MMGAN,MMT,ModImp,M2DN,Zhang}.pt |
| │ └── mri_ct/{MMSyn,MMGAN,MMT,ModImp,M2DN,Zhang}.pt |
| └── unet3d/brats.pt # 3D U-Net segmenter |
| data/ |
| ├── brats_{train_00,train_01,train_02,val,test}.tar |
| └── mri_ct_{train,val,test}.tar |
| ``` |
|
|
| ### Usage |
|
|
| Run from the root of the code repository. |
|
|
| ``` |
| hf download WTHH031230/SphereFlow --include "checkpoints/*" --local-dir . |
| hf download WTHH031230/SphereFlow --include "data/*" --local-dir . |
| for f in data/*.tar; do tar -xf "$f" -C /path/to/processed; done |
| DATA_ROOT=/path/to/processed bash scripts/preprocess/make_splits.sh --check |
| ``` |
|
|
| ### Data |
|
|
| The `.npy` slices were produced from the following datasets with the preprocessing scripts in `scripts/preprocess/` of the code repository. They are released under CC BY-NC 4.0, for non-commercial use only. |
|
|
| - **BraTS 2023 adult glioma (BraTS-GLI)**, CC BY-NC 4.0. Data used in this publication were obtained as part of the Brain Tumor Segmentation (BraTS) Challenge project through Synapse ID: [syn51156910](https://www.synapse.org/brats2023). Please cite: |
| - U. Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification, arXiv:2107.02314, 2021. |
| - B. H. Menze, et al., The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), IEEE TMI 34(10), 2015. |
| - S. Bakas, et al., Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features, Scientific Data 4:170117, 2017. |
| - A. Karargyris, et al., Federated benchmarking of medical artificial intelligence with MedPerf, Nature Machine Intelligence 5:799–810, 2023. |
| - **SynthRAD2023, Task 1 (brain MRI ↔ CT)**, CC BY-NC 4.0, [Zenodo 10.5281/zenodo.7260705](https://doi.org/10.5281/zenodo.7260705). Please cite: |
| - A. Thummerer, et al., SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy, Medical Physics 50(7), 2023. |
|
|
| ### Citation |
|
|
| @inproceedings{wang2026sphereflow, |
| title = {SphereFlow: Missing Modality Imputation via Geometric Transport on Hypersphere}, |
| author = {Wang, Huihan and Wang, Juncheng and Feng, Yuxiang and Hou, Wenlong and Yu, Guoqi and Xu, Chao and Liu, Yang and Sun, Baigui and Wang, Shujun}, |
| booktitle = {Advances in Neural Information Processing Systems}, |
| year = {2026} |
| } |
| |
