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README.md

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 ---
 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}
+}
+```

mass_base.pth

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