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AutoMedBench-Full-release

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Dataset Viewer

The dataset viewer is not available for this subset.

Cannot get the split names for the config 'default' of the dataset.

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 91, in _split_generators
                  inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
                                          ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 6320, in pyarrow.lib.concat_tables
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                  return check_status(status)
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowTypeError: Unable to merge: Field json has incompatible types: struct<manifests: list<item: struct<annotations: struct<config.digest: string, io.containerd.image.name: string, org.opencontainers.image.ref.name: string>, digest: string, mediaType: string, size: int64>>, mediaType: string, schemaVersion: int64> vs list<item: struct<Config: string, Layers: list<item: string>, RepoTags: list<item: string>>>
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

AutoMedBench-Full-release

中文说明

AutoMedBench is a workflow-aware benchmark for autonomous medical-AI research agents. It evaluates both the final task artifact and the S1-S5 research workflow: Plan, Setup, Validate, Inference, and Submit.

This Full release packages 48 tasks across 7 tracks with Lite and Standard tiers, for 96 task-tier combinations. Each combination has a pre-built Docker image. Dataset bytes are not bundled — users prepare data independently and mount it at runtime.

How to Submit

Submission email: jliu718@ucsc.edu

Suggested subject: AutoMedBench submission - <agent/team name>

Priority	What to do	Why it matters
Required	Run your agent at least 5 repeated times on the benchmark. Agent runs can vary because of model sampling, tool-use choices, network timing, and dependency setup. Repeated runs provide a more stable estimate of real performance than a single run.
Required	Zip all output reports and conversation/transcript files into one archive, then email that zip file to jliu718@ucsc.edu.	The full package lets maintainers verify final artifacts, audit the workflow, and check stability across repeated runs.
Recommended	Use a coding agent to launch, monitor, and collect runs.	Benchmark runs are long and may need log inspection, quick fixes, and clean collection of conversation files.

Quick Start

# 1. Download a track Docker image (e.g. segmentation)
#    From this repository: docker/segmentation/automedbench-segmentation-docker-image-v0.1.0.tar.gz

# 2. Load the Docker image
docker load -i automedbench-segmentation-docker-image-v0.1.0.tar.gz

# 3. Set up credentials
cp env.example .env
# edit .env with your API key

# 4. Run a task
python docker/orchestrator.py \
    --agent claude-opus-4-6 \
    --task kidney-seg-task \
    --tier lite \
    --n-patients 20

The Docker image contains the benchmark harness and scoring code. Credentials are passed only at runtime through .env or shell environment variables. Dataset files must be mounted separately (see Data Acquisition below).

Docker Image Packages

Prebuilt per-track Docker image packages are stored in this repository:

docker/classification/automedbench-classification-docker-image-v0.1.0.tar.gz
docker/detection/automedbench-detection-docker-image-v0.1.0.tar.gz
docker/enhancement/automedbench-enhancement-docker-image-v0.1.0.tar.gz
docker/report/automedbench-report-docker-image-v0.1.0.tar.gz
docker/segmentation/automedbench-segmentation-docker-image-v0.1.0.tar.gz
docker/synthesis/automedbench-synthesis-docker-image-v0.1.0.tar.gz
docker/vqa/automedbench-vqa-docker-image-v0.1.0.tar.gz

Each archive is ~3.5 GB and contains all Lite + Standard task-tier images for that track. The shared runtime archive (runtime/automedbench-full-runtime-0.1.0.tar.gz, ~4 GB) supplies the common agent runtime and seven track evaluator images.

After downloading, load all images:

docker load -i runtime/automedbench-full-runtime-0.1.0.tar.gz
docker load -i docker/segmentation/automedbench-segmentation-docker-image-v0.1.0.tar.gz
# ... repeat for other tracks as needed

Released Tasks (48 tasks, 96 Lite/Standard combinations)

Track	Tasks	Metric focus
Segmentation	14	Dice (organ + lesion)
Enhancement	7	SSIM
VQA	9	Answer accuracy
Report generation	5	Clinical report quality
Detection	4	mAP@0.5
Classification	5	Label accuracy
Synthesis	4	SSIM

Data Acquisition

Dataset bytes are not included in this release. Each task references an upstream dataset that must be downloaded independently. The table below lists every task's data source, download link, license, and access requirements.

Segmentation (14 tasks)

Task ID	Upstream Dataset	Download Link	License	Access	Approx. Size	Status
`kidney-seg-task`	KiTS19	kits19.grand-challenge.org	CC BY-NC-SA 4.0	Public	~200 MB/case	Acquisition ready
`pancreas-seg-task`	PanTS	github.com/MrGiovanni/PanTS	CC BY-NC-ND 4.0	Public	~300–346 GB	Acquisition ready
`pancreas-oar-seg-task`	PanTS	github.com/MrGiovanni/PanTS	CC BY-NC-ND 4.0	Public	~300–346 GB	Acquisition ready
`liver-seg-task`	MSD Task03 Liver	medicaldecathlon.com	Provenance conflict	Public	~28.9 GB	Staged verified
`aeropath-seg-task`	AeroPath	zenodo.org/records/10069289	CC BY 4.0	Public	~5.04 GB	Acquisition ready
`tsg-multiorgan-seg-task`	TotalSegmentator v2.0.1	zenodo.org/records/10047263	CC BY 4.0	Public	~3.24 GB	Staged verified
`colon-seg-task`	MSD Task10 Colon	medicaldecathlon.com	CC BY-SA 4.0	Public	~6.24 GB	Staged verified
`hepaticvessel-seg-task`	MSD Task08 HepaticVessel	medicaldecathlon.com	CC BY-SA 4.0	Public	~9.35 GB	Staged verified
`spleen-seg-task`	MSD Task09 Spleen	medicaldecathlon.com	CC BY-SA 4.0	Public	~1.61 GB	Staged verified
`heart-seg-task`	MSD Task02 Heart	medicaldecathlon.com	CC BY-SA 4.0	Public	~456 MB	Staged verified
`prostate-seg-task`	MSD Task05 Prostate	medicaldecathlon.com	CC BY-SA 4.0	Public	~240 MB	Staged verified
`feta-seg-task`	FeTA	zenodo.org/records/4541606	Research agreement	Gated	—	Externally blocked
`panther-t1-seg-task`	PANTHER	zenodo.org/records/15192302	CC BY-NC 4.0 + access controls	Restricted	—	Externally blocked
`panther-t2-seg-task`	PANTHER	zenodo.org/records/15192302	CC BY-NC 4.0 + access controls	Restricted	—	Externally blocked

Enhancement (7 tasks)

Task ID	Upstream Dataset	Download Link	License	Access	Approx. Size	Status
`ldct-denoising-task`	AAPM Low Dose CT Grand Challenge	aapm.org/grandchallenge/lowdosect	No explicit redistribution grant	Public library	~42 MB staged	Download required
`lidc-idri-denoising-task`	LIDC-IDRI	cancerimagingarchive.net/collection/lidc-idri	CC BY 3.0	Public	~136.55 GB	Acquisition ready
`deeplesion-denoising-task`	NIH DeepLesion	nihcc.app.box.com/v/DeepLesion	No explicit license	Public	Unknown	Acquisition ready
`mri-sr-task`	fastMRI	fastmri.med.nyu.edu	Agreement prohibits redistribution	Application required	—	Externally blocked
`brats-t1c-sr-task`	BraTS 2023 GLI	synapse.org/Synapse:syn51156910	Controlled access	Controlled	—	Externally blocked
`ixi-t1-sr-task`	IXI T1	brain-development.org/ixi-dataset	CC BY-SA 3.0	Public	Not specified	Acquisition ready
`nih-cxr-sr-task`	NIH ChestXray14	nihcc.app.box.com/v/ChestXray-NIHCC	CC0 (NIH Kaggle)	Public	~45.08 GB	Acquisition ready

VQA (9 tasks)

Task ID	Upstream Dataset	Download Link	License	Access	Approx. Size	Status
`pathvqa-task`	PathVQA	huggingface.co/datasets/flaviagiammarino/path-vqa	MIT	Public	~785 MB	Staged verified
`vqa-rad-task`	VQA-RAD	huggingface.co/datasets/flaviagiammarino/vqa-rad	CC0 1.0	Public	~34.5 MB	Staged verified
`medframeqa-task`	MedFrameQA	huggingface.co/datasets/SuhaoYu1020/MedFrameQA	CC BY 4.0	Public	~571 MB	Staged verified
`slake-task`	SLAKE	huggingface.co/datasets/BoKelvin/SLAKE	CC BY 4.0	Public	~217 MB	Staged verified
`medxpertqa-mm-task`	MedXpertQA	huggingface.co/datasets/TsinghuaC3I/MedXpertQA	MIT	Public	~523 MB	Staged verified
`vqa-kvasir-task`	Kvasir-VQA	huggingface.co/datasets/SimulaMet-HOST/Kvasir-VQA	CC BY-NC 4.0 + benchmark permission	Permission required	~15.18 GB	Externally blocked
`vqa-omnimedvqa-task`	OmniMedVQA	huggingface.co/datasets/foreverbeliever/OmniMedVQA	Mixed (no global license)	Mixed access	~10.70 GB	Externally blocked
`vqa-pmc-vqa-task`	PMC-VQA	huggingface.co/datasets/RadGenome/PMC-VQA	CC BY-SA	Public	~21.77 GB	Staged verified
`vqa-mmmu-medical-task`	MMMU medical subsets	huggingface.co/datasets/MMMU/MMMU	Apache-2.0	Public	~2.09 GB	Staged verified

Report Generation (5 tasks)

Task ID	Upstream Dataset	Download Link	License	Access	Approx. Size	Status
`mimic-cxr-report-task`	MIMIC-CXR	physionet.org/content/mimic-cxr/2.1.0	DUA prohibits sharing	Credentialed DUA	377,110 images	Externally blocked
`iu-xray-report-task`	IU Open-i	openi.nlm.nih.gov	No clear bulk redistribution license	Public search	Unknown	Acquisition ready
`chexpert-plus-cxr-task`	CheXpert Plus	aimi.stanford.edu/datasets/chexpert-plus	No license declared by mirror	Public mirror	~636 MB	Staged verified
`pathology-caption-100-task`	PathCap	huggingface.co/datasets/jamessyx/PathCap	CC BY-NC 2.0 + click-through	Auto-gated	~13.7 GB	Staged verified
`pathology-caption-500-task`	PathCap	huggingface.co/datasets/jamessyx/PathCap	CC BY-NC 2.0 + click-through	Auto-gated	~13.7 GB	Staged verified

Detection (4 tasks)

Task ID	Upstream Dataset	Download Link	License	Access	Approx. Size	Status
`vindr-cxr-det-task`	VinDr-CXR	physionet.org/content/vindr-cxr/1.0.0	PhysioNet Credentialed Health Data License	Credentialed DUA	—	Externally blocked
`bccd-det-task`	BCCD	github.com/Shenggan/BCCD_Dataset	MIT	Public	~4.95 MB	Staged verified
`dentex-det-task`	DENTEX	huggingface.co/datasets/ibrahimhamamci/DENTEX	CC BY-NC-SA 4.0	Public	~11.84 GB	Staged verified
`grazpedwri-det-task`	GRAZPEDWRI-DX	figshare.com/articles/dataset/GRAZPEDWRI-DX/14825193	CC BY 4.0	Public	~16.26 GB	Staged verified

Classification (5 tasks)

Task ID	Upstream Dataset	Download Link	License	Access	Approx. Size	Status
`braintumor-cls-task`	Brain Tumor MRI	kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset	CC BY 4.0	Public	~167.85 MB	Acquisition ready
`crc-histology-cls-task`	NCT-CRC-HE-100K	zenodo.org/records/1214456	CC BY 4.0	Public	~11.7 GB	Acquisition ready
`patchcamelyon-cls-task`	PatchCamelyon	github.com/basveeling/pcam	CC0	Public	~7.7 GB	Acquisition ready
`chest-xray-pneumonia-cls-task`	Kermany chest X-ray	data.mendeley.com/datasets/rscbjbr9sj/2	CC BY 4.0	Public	Not specified	Acquisition ready
`skin-lesion-cls-task`	ISIC Archive	isic-archive.com	Per-image (not frozen)	Public	~6.26 MB staged	Staged verified

Synthesis (4 tasks)

Task ID	Upstream Dataset	Download Link	License	Access	Approx. Size	Status
`synthrad2025-mrct-task`	SynthRAD2025	zenodo.org/records/15373853	CC BY-NC 4.0	Public	~14.8 GB	Acquisition ready
`ctorg-ctsr-task`	CT-ORG	cancerimagingarchive.net/collection/ct-org	CC BY 3.0	Public	Not exposed	Acquisition ready
`msd-pancreas-ctsr-task`	MSD Task07 Pancreas	medicaldecathlon.com	CC BY-SA 4.0	Public	~12.3 GB	Staged verified
`totalsegmentator-ctsr-task`	TotalSegmentator	github.com/wasserth/TotalSegmentator	CC BY 4.0	Public	Not frozen	Acquisition ready

Data Acquisition Summary

Status	Count	Description
Staged verified	23	Locally staged and layout-hashed; recipe frozen
Acquisition ready	16	Source and staging recipe frozen; user must download
Externally blocked	9	Access approval, DUA, or upstream clarification required

Dataset Licenses

See docs/DATA_POLICY.md for the full per-task policy records. The release rule is conservative: no dataset bytes are embedded in any task package, runtime image, or task-tier image. Every uploaded artifact is code-only.

Package Design

Each of the 48 task packages supports both lite and standard tiers; the tiers share the same harness and runtime contract. The common container layer remains eight shared, data-free runtime images:

one common agent runtime used by every track; and
seven track-specific evaluator runtimes, one for each track.

The execution release adds 96 thin images on top of that shared layer: one immutable image for every task-tier cell. Their build IDs and API verification records are frozen in manifests/cell_images.csv and manifests/cell_verification.csv. The shared image inventory remains frozen in manifests/runtime_images.csv.

Following AutoMedBench-Lite, the 96 images are exported under seven track folders as docker/<track>/automedbench-<track>-docker-image-v0.1.0.tar.gz; the local synthetic track uses Lite's public synthesis folder name. Each archive is accompanied by a SHA-256 sidecar and an image inventory. Exact remote sizes, hashes, and revisions are frozen in manifests/cell_archives.csv.

Security

No credentials, API keys, model-service tokens, personal home paths, or populated runtime configuration belong in this repository or its images. Source worktrees are read-only inputs; builds use fresh allow-listed staging contexts made from immutable Git blobs.

Paper

Project page: https://automedbench.github.io/

Paper: Junqi Liu et al., "AutoMedBench: Towards Medical AutoResearch with Agentic AI Models", arXiv:2606.01961, 2026. https://arxiv.org/abs/2606.01961

DOI: https://doi.org/10.48550/arXiv.2606.01961

@article{liu2026automedbench,
  title={AutoMedBench: Towards Medical AutoResearch with Agentic AI Models},
  author={Liu, Junqi and Song, Selena and Wang, Yuhan and Mao, Jiawei and Chen, Hardy and Huang, Xiaoke and Qi, Tianhao and Guo, Pengfei and Tang, Yucheng and He, Yufan and Zhao, Can and Myronenko, Andriy and Yang, Dong and Xu, Daguang and Zhou, Yuyin},
  journal={arXiv preprint arXiv:2606.01961},
  year={2026}
}

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Paper for MitakaKuma/AutoMedBench-Full-release

AutoMedBench: Towards Medical AutoResearch with Agentic AI Models

Paper • 2606.01961 • Published Jun 3 • 28