---
license: mit
library_name: pytorch
tags:
  - medical-image-segmentation
  - 3d-medical-imaging
  - self-supervised-learning
  - in-context-segmentation
  - pytorch
  - arxiv:2603.13660
pipeline_tag: image-segmentation
---

# MASS Base Checkpoint

This repository hosts `mass_base.pth`, the base checkpoint for **MASS: Learning
Generalizable 3D Medical Image Representations from Mask-Guided
Self-Supervision**.

MASS is a mask-guided self-supervised learning framework for 3D medical images.
The released checkpoint was trained with the data used in our paper and the Iris
in-context segmentation architecture. It uses automatically generated
class-agnostic masks for pretraining and does **not** use expert ground-truth
annotations during pretraining.

## What This Checkpoint Is For

`mass_base.pth` can be used with the official MASS codebase for:

- training-free in-context segmentation with reference image-mask examples;
- initialization for downstream segmentation finetuning;
- frozen-encoder or finetuned encoder classification experiments.

This is a PyTorch checkpoint for the MASS/Iris architecture, not a standalone
Transformers model. Please use it with the code release:

- GitHub: https://github.com/Stanford-AIMI/MASS
- Project page: https://yhygao.github.io/MASS_page/
- Paper: https://arxiv.org/abs/2603.13660

## Download

Using the Hugging Face CLI:

```bash
hf download StanfordAIMI/MASS mass_base.pth --local-dir checkpoints
```

Using Python:

```python
from huggingface_hub import hf_hub_download

checkpoint_path = hf_hub_download("StanfordAIMI/MASS", "mass_base.pth")
```

## Raw NIfTI In-Context Inference

```bash
python inference.py \
  --checkpoint checkpoints/mass_base.pth \
  --test-image /path/to/test_image.nii.gz \
  --reference-image /path/to/reference_image.nii.gz \
  --reference-mask /path/to/reference_mask.nii.gz \
  --output outputs/test_image_seg.nii.gz \
  --gpu 0 \
  --use-ema \
  --modality ct \
  --orientation RAS \
  --target-spacing 1.5 1.5 1.5 \
  --window-size 128 128 128 \
  --overlap 0.5
```

Please make sure the input NIfTI metadata is complete and reliable, especially
orientation and spacing. `mass_base.pth` was trained after standardizing images
to RAS orientation, so using `--orientation RAS` is recommended.

## Downstream Segmentation Finetuning

```bash
python train.py \
  --config config/downstream/segmentation_finetune_example.yaml \
  --gpu 0 \
  --name segmentation_finetune_example \
  --override \
    finetuning.pretrained_checkpoint=checkpoints/mass_base.pth \
    data.train.data_root=/path/to/mass_h5 \
    data.val.data_root=/path/to/mass_h5 \
    data.train.datasets='[example_segmentation]' \
    data.val.datasets='[example_segmentation]'
```

## Classification Linear Probing

```bash
python train.py \
  --config config/downstream/classification_linear_probe_example.yaml \
  --gpu 0 \
  --name classification_linear_probe_example \
  --override \
    classification.encoder.pretrained_checkpoint=checkpoints/mass_base.pth \
    classification.num_classes=2 \
    data.train.data_root=/path/to/classification_data \
    data.val.data_root=/path/to/classification_data \
    data.train.datasets='[example_classification]' \
    data.val.datasets='[example_classification]'
```

## Training Details

- Architecture: Iris in-context segmentation architecture.
- Pretraining objective: MASS mask-guided self-supervised learning.
- Supervision during pretraining: automatically generated class-agnostic masks.
- Expert annotations during pretraining: none.
- Modalities: 3D CT, MRI, and PET volumes used in the MASS paper.

The MASS objective is compatible with other in-context segmentation
architectures. The official codebase includes preprocessing and pretraining
utilities for training MASS on your own data.

## Limitations

- This checkpoint is intended for research use.
- It is not a medical device and should not be used for clinical decision-making.
- Raw NIfTI inference depends on reliable image metadata and preprocessing
  choices. Cases with missing or incorrect spacing/orientation metadata should be
  inspected carefully.
- Task-specific finetuning or validation is recommended before using the model on
  a new dataset or anatomy.

## Citation

```bibtex
@article{gao2026learning,
  title={Learning Generalizable 3D Medical Image Representations from Mask-Guided Self-Supervision},
  author={Gao, Yunhe and Zhang, Yabin and Wang, Chong and Liu, Jiaming and Varma, Maya and Delbrouck, Jean-Benoit and Chaudhari, Akshay and Langlotz, Curtis},
  journal={arXiv preprint arXiv:2603.13660},
  year={2026}
}
```