---
pretty_name: FAME Benchmark
license: other
task_categories:
- image-segmentation
tags:
- medical-image-segmentation
- few-shot-segmentation
- semantic-segmentation
- benchmark
size_categories:
- 10K<n<100K
configs:
- config_name: preview
  data_files:
  - split: preview
    path: viewer/fame_preview.parquet
- config_name: pixels
  data_files:
  - split: full
    path: data_shards/fame_image_mask_pairs-*.parquet
---

# FAME Benchmark

FAME is a benchmark for zero-shot, few-shot, and OOD medical image segmentation. It is introduced in [arXiv:2607.27856](https://arxiv.org/abs/2607.27856): **Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation**.

If you use this benchmark, please cite the FAME paper. The BibTeX entry is provided in the [Citation](#citation) section.

This Hugging Face dataset contains the released FAME JSON/JSONL protocols plus all referenced image and mask pixels. To keep the repository usable on Hugging Face, the protocols are packaged in `protocols/FAME_benchmark_protocols.tar.gz`, and the pixels are stored as Parquet shards with `image` and `mask` columns so samples can be inspected in Dataset Viewer/Data Studio. After extracting the protocol archive, `tools/extract_pixels_from_parquet.py` can materialize the raw image and mask files referenced by the protocol JSONL files.

## What Is Segmented?

The montage below shows representative positive target masks from the released FAME tasks. Magenta indicates the foreground mask and yellow indicates the mask contour.

![Representative FAME segmentation targets](assets/fame_segmentation_targets_overview.png)

## Directory Layout

```text
assets/
  fame_segmentation_targets_overview.png
protocols/
  FAME_benchmark_protocols.tar.gz

After extracting protocols/FAME_benchmark_protocols.tar.gz:

FAME_benchmark/
  tasks/<task>/
    task_info.json
    train_pool.jsonl
    train_positive.jsonl
    train_negative.jsonl
    test.jsonl
    test_positive.jsonl
    test_negative.jsonl
  episodes/k10_seed{0..4}/<task>/
    support.jsonl
    train.jsonl
    test.jsonl
  episodes/zero_shot/<task>/
    test.jsonl
  ood/FAME_ood_pairs.csv
  ood_episodes/k10_seed0/<pair_id>/
    support.jsonl
    test.jsonl
    ood_pair_info.json
  task_lists/FAME_benchmark_tasks.txt
  task_stats.csv
data_shards/
  fame_image_mask_pairs-*.parquet
viewer/
  fame_preview.parquet
metadata/
  protocol_summary.json
  pixel_shard_manifest.csv
  image_mask_pair_manifest.csv
  iid_dataset_summary.csv
  ood_pair_summary.csv
tools/
  load_fame.py
  extract_pixels_from_parquet.py
```

## Protocols

Extract the protocol archive first:

```bash
tar -xzf protocols/FAME_benchmark_protocols.tar.gz
```

Zero-shot evaluation uses the frozen query set for each IID ROI task and no support masks:

```text
FAME_benchmark/episodes/zero_shot/<task>/test.jsonl
```

Few-shot evaluation uses 10 positive support examples per task with five fixed seeds:

```text
FAME_benchmark/episodes/k10_seed0/<task>/
FAME_benchmark/episodes/k10_seed1/<task>/
FAME_benchmark/episodes/k10_seed2/<task>/
FAME_benchmark/episodes/k10_seed3/<task>/
FAME_benchmark/episodes/k10_seed4/<task>/
```

OOD evaluation uses 10 support examples from the source task and evaluates on a target task under covariate or semantic shift:

```text
FAME_benchmark/ood/FAME_ood_pairs.csv
FAME_benchmark/ood_episodes/k10_seed0/<pair_id>/
```

## Dataset Summary

- IID ROI tasks: 21
- IID source datasets: 19/19
- IID test samples: 14,958 total, 13,689 positive, 1,269 negative
- Pixel pairs: 78431 image/mask pairs
- Pixel shards: 77 Parquet files
- Organs: brain, breast, colon, lung, retina, skin, thyroid
- Modalities: CT, MRI, MRI_adc, OCT, colonoscopy, color_fundus_photography, dermoscopy, histopathology, ultrasound

## Pixel Storage

Each row in `data_shards/fame_image_mask_pairs-*.parquet` contains:

- `image`: image bytes and the original repository-relative path
- `mask`: mask bytes and the original repository-relative path
- `image_path` and `mask_path`: paths referenced by the JSON/JSONL protocols
- task-level metadata such as dataset, organ, modality, task, target, and case id

The `preview` config is small and intended for quick visual inspection. The `pixels` config contains the full image/mask pixel store.

## Materialize Raw Files

To run code that expects the JSON paths and raw pixel files to exist on disk, extract the protocols and then extract the Parquet pixel store:

```bash
tar -xzf protocols/FAME_benchmark_protocols.tar.gz

python tools/extract_pixels_from_parquet.py \
  --root /path/to/FAME-benchmark \
  --out-root /path/to/FAME-benchmark
```

This creates the `data/benchmark_dataset/...` and `data/benchmark_dataset_ood/...` image/mask files referenced by the protocol JSONL files.

## Load Episodes in Python

```python
from pathlib import Path
from tools.load_fame import list_ood_pairs, list_tasks, load_episode, load_ood_episode

release_root = Path("/path/to/FAME-benchmark")

tasks = list_tasks(release_root)
support, query = load_episode(
    release_root,
    "ISIC2018__ISIC2018_skin_lesion",
    seed=0,
    resolve_paths=False,
)
print(len(support), len(query))
print(query[0]["image"], query[0]["mask"])

ood_pairs = list_ood_pairs(release_root)
ood_support, ood_query, ood_info = load_ood_episode(
    release_root,
    "cov_BUSI_to_BUID_breast_mass",
    resolve_paths=False,
)
print(ood_info["shift_type"], len(ood_support), len(ood_query))
```

Set `resolve_paths=True` after running `tools/extract_pixels_from_parquet.py`.

## Run STAMP-2B or MedSAM3

```bash
cd /path/to/Disease-conditionalSeg
export FAME_RELEASE=/path/to/FAME-benchmark

tar -xzf "${FAME_RELEASE}/protocols/FAME_benchmark_protocols.tar.gz" \
  -C "${FAME_RELEASE}"

python "${FAME_RELEASE}/tools/extract_pixels_from_parquet.py" \
  --root "${FAME_RELEASE}" \
  --out-root "${FAME_RELEASE}"

ln -sfn "${FAME_RELEASE}/data" ./data

export POOL_ROOT="${FAME_RELEASE}/FAME_benchmark"
export EPISODE_ROOT="${FAME_RELEASE}/FAME_benchmark/episodes"
export TASKS="${FAME_RELEASE}/FAME_benchmark/task_lists/FAME_benchmark_tasks.txt"
export K=10
export SEED=0
export GPUS=0,1,2,3

bash scripts/bench/run/download_weights.sh check

bash scripts/bench/run/stamp.sh 2b_in_context
bash scripts/bench/run/stamp.sh 2b_zero_disease

bash scripts/bench/run/medsam3.sh ten_support
bash scripts/bench/run/medsam3.sh zero_disease
```

## License and Data Access

FAME is provided for non-clinical research benchmarking. The underlying medical images and annotations remain governed by the licenses and access conditions of their original datasets.

## Citation

If you use FAME, please cite:

```bibtex
@misc{liu2026benchmarkingfoundationlargelanguage,
      title={Benchmarking Foundation and Large Language Models for Few-Shot Medical Image Segmentation},
      author={Jinghong Liu and Yuchuan Deng and Fanping Liu and Meng Huang and Xirong Li},
      year={2026},
      eprint={2607.27856},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2607.27856},
}
```