Datasets:

leaf0932
/

CausalMedBench

---
license: c-uda
language:
- en
task_categories:
- image-classification
- visual-question-answering
tags:
- medical
- deepfake-detection
- multimodal-reasoning
- counterfactual-evaluation
- explainability
pretty_name: CausalMedBench
size_categories:
- 10K<n<100K
configs:
- config_name: five_view
  default: true
  data_files:
  - split: train
    path: five_view/train-*.parquet
  - split: validation
    path: five_view/validation-*.parquet
  - split: test
    path: five_view/test-*.parquet
- config_name: ordinary
  data_files:
  - split: test
    path: ordinary/test-*.parquet
---
# CausalMedBench

CausalMedBench is an independent downstream benchmark created for this project.
It was not proposed or released as part of the MedForge paper or the official
MedForge release. CausalMedBench evaluates whether a medical deepfake detector's
final decision and explanation are supported by the visual evidence it claims
to use.

The upstream authentic images, forged images, manipulation boxes, and
real--forged pairing metadata come from MedForge-90K. CausalMedBench introduces
the five causal views, fixed splits, evidence-dependency tasks, explanation
faithfulness protocol, evaluation metrics, and public scorer. It contains 8,212
source-linked groups (41,060 five-view examples) across brain MRI, chest X-ray,
and fundus imaging.

The fixed held-out benchmark contains 1,840 groups (9,200 five-view examples),
an ordinary detection set of 6,501 images, and a stratified explanation subset
of 300 groups (1,500 examples).

## Five causal views

Each group contains:

1. `forged_original`: the original forged image.
2. `authentic_source`: the corresponding image before manipulation.
3. `local_restored`: the forged image with the annotated manipulated box
   restored from the authentic source; the rest of the forged image is unchanged.
4. `local_forged_only`: the authentic image with only the annotated forged box
   inserted; the rest of the image is authentic.
5. `authentic_nuisance`: the authentic image after a deterministic mild JPEG,
   blur, denoising, or resize-resampling perturbation that preserves its medical
   content.

Together, these views test ordinary detection, local evidence sufficiency,
global/outside evidence sufficiency, benign-perturbation invariance, five-view
consistency, and explanation–evidence alignment.

## Load the benchmark

```python
from datasets import load_dataset
five_view = load_dataset("leaf0932/CausalMedBench", "five_view")
ordinary = load_dataset("leaf0932/CausalMedBench", "ordinary", split="test")
print(five_view)
print(five_view["test"][0])
```

The `five_view` configuration has `train`, `validation`, and `test` splits. The
`ordinary` configuration contains the fixed 6,501-image detection test split.
Images are embedded in Parquet shards, so no local MedForge path is required.

## Prediction format

Classification predictions are JSONL records keyed by `record_id`:

```json
{"record_id":"pair_001174f8d03bfe37::forged_original","verdict":"FAKE"}
```

For the 1,500 records where `is_reasoning_subset` is `true`, structured
explanations use:

```json
{"record_id":"pair_001174f8d03bfe37::forged_original","verdict":"FAKE","evidence_type":"mixed","evidence_location":"mixed","evidence":"The localized anatomy is inconsistent and the surrounding texture also differs."}
```

Allowed evidence types are `none`, `local_medical`, `local_forensic`,
`local_medical_and_local_forensic`, `global_forensic`, and `mixed`. Allowed
locations are `none`, `local_bbox`, `global_or_outside`, and `mixed`.

## Score predictions

```bash
git clone https://huggingface.co/datasets/leaf0932/CausalMedBench
cd CausalMedBench
python -m pip install -r requirements.txt
python evaluation/score.py \
  --ordinary-predictions /path/to/ordinary_predictions.jsonl \
  --five-view-predictions /path/to/five_view_predictions.jsonl \
  --reasoning-predictions /path/to/reasoning_predictions.jsonl \
  --output metrics.json
```

The scorer reports:

- ordinary Accuracy and Macro-F1, including modality, forgery-type, and
  generator breakdowns;
- five-view accuracy and all-five-correct consistency;
- local and global evidence sufficiency;
- benign nuisance invariance and authentic recovery;
- explanation verdict, evidence-type, evidence-location, and joint alignment.

Run `python evaluation/score.py --help` for local dataset and strict-coverage
options. A submission template is included in `examples/`.

## Dataset fields

Important fields include `record_id`, `group_id`, `view_id`, `target_label`,
`image`, `bbox_normalized_1000`, `modality`, `forgery_type`, `generator`,
`disease`, `operation`, `evidence_available`, `evidence_removed`, and
`is_reasoning_subset`. Bounding boxes use a 0–1000 normalized coordinate system.

The released masks are rectangular box rasterizations, not pixel-precise
segmentation masks. The locally composed views use an 8-pixel feathered box
boundary. Nuisance operations are deterministic and recorded per row.

## Source and license

CausalMedBench uses source data from
[`RichardChenZH/MedForge-90K`](https://huggingface.co/datasets/RichardChenZH/MedForge-90K).
The data, including redistributed and derived images, is released under the
Computational Use of Data Agreement (C-UDA). Users must comply with C-UDA and
preserve the upstream attribution. Benchmark code in `evaluation/` and `scripts/`
is provided under Apache-2.0. See `LICENSE` and `DATA_LICENSE.md` for details.

The MedForge citation acknowledges the upstream data source only. It does not
attribute CausalMedBench, its five-view construction, its evaluation protocol,
or experiments conducted with CausalMedBench to the MedForge authors.

This benchmark is for research and educational use. It is not a clinical
diagnostic tool and must not be used to make patient-care decisions.

## Citation

Please cite CausalMedBench for the benchmark, five-view construction, evaluation
protocol, metrics, and scorer:

```bibtex
@misc{you2026causalmedbench,
  title={CausalMedBench: Evaluating Evidence Dependency and Explanation Faithfulness in Medical Deepfake Reasoning},
  author={You, Maowei},
  year={2026},
  howpublished={Hugging Face dataset},
  url={https://huggingface.co/datasets/leaf0932/CausalMedBench},
  version={1.0.0}
}
```

The manuscript citation will replace the repository citation after the paper is
publicly archived. Because the released images and source annotations originate
from MedForge-90K, also cite MedForge as the upstream data source:

```bibtex
@misc{chen2026medforge90k,
  title={MedForge-90K},
  author={Chen, Zhihui and He, Kai and Lei, Qingyuan and Pu, Bin and Zhang, Jian and Xu, Yuling and Feng, Mengling},
  year={2026},
  howpublished={Hugging Face dataset},
  url={https://huggingface.co/datasets/RichardChenZH/MedForge-90K},
  note={Upstream source dataset used to construct CausalMedBench}
}
```

These two citations serve different purposes: the first credits CausalMedBench;
the second preserves the required attribution for the MedForge-90K source data.

	---
	license: c-uda
	language:
	- en
	task_categories:
	- image-classification
	- visual-question-answering
	tags:
	- medical
	- deepfake-detection
	- multimodal-reasoning
	- counterfactual-evaluation
	- explainability
	pretty_name: CausalMedBench
	size_categories:
	- 10K<n<100K
	configs:
	- config_name: five_view
	default: true
	data_files:
	- split: train
	path: five_view/train-*.parquet
	- split: validation
	path: five_view/validation-*.parquet
	- split: test
	path: five_view/test-*.parquet
	- config_name: ordinary
	data_files:
	- split: test
	path: ordinary/test-*.parquet
	---

	# CausalMedBench

	CausalMedBench is an independent downstream benchmark created for this project.
	It was not proposed or released as part of the MedForge paper or the official
	MedForge release. CausalMedBench evaluates whether a medical deepfake detector's
	final decision and explanation are supported by the visual evidence it claims
	to use.

	The upstream authentic images, forged images, manipulation boxes, and
	real--forged pairing metadata come from MedForge-90K. CausalMedBench introduces
	the five causal views, fixed splits, evidence-dependency tasks, explanation
	faithfulness protocol, evaluation metrics, and public scorer. It contains 8,212
	source-linked groups (41,060 five-view examples) across brain MRI, chest X-ray,
	and fundus imaging.

	The fixed held-out benchmark contains 1,840 groups (9,200 five-view examples),
	an ordinary detection set of 6,501 images, and a stratified explanation subset
	of 300 groups (1,500 examples).

	## Five causal views

	Each group contains:

	1. `forged_original`: the original forged image.
	2. `authentic_source`: the corresponding image before manipulation.
	3. `local_restored`: the forged image with the annotated manipulated box
	restored from the authentic source; the rest of the forged image is unchanged.
	4. `local_forged_only`: the authentic image with only the annotated forged box
	inserted; the rest of the image is authentic.
	5. `authentic_nuisance`: the authentic image after a deterministic mild JPEG,
	blur, denoising, or resize-resampling perturbation that preserves its medical
	content.

	Together, these views test ordinary detection, local evidence sufficiency,
	global/outside evidence sufficiency, benign-perturbation invariance, five-view
	consistency, and explanation–evidence alignment.

	## Load the benchmark

	```python
	from datasets import load_dataset

	five_view = load_dataset("leaf0932/CausalMedBench", "five_view")
	ordinary = load_dataset("leaf0932/CausalMedBench", "ordinary", split="test")

	print(five_view)
	print(five_view["test"][0])
	```

	The `five_view` configuration has `train`, `validation`, and `test` splits. The
	`ordinary` configuration contains the fixed 6,501-image detection test split.
	Images are embedded in Parquet shards, so no local MedForge path is required.

	## Prediction format

	Classification predictions are JSONL records keyed by `record_id`:

	```json
	{"record_id":"pair_001174f8d03bfe37::forged_original","verdict":"FAKE"}
	```

	For the 1,500 records where `is_reasoning_subset` is `true`, structured
	explanations use:

	```json
	{"record_id":"pair_001174f8d03bfe37::forged_original","verdict":"FAKE","evidence_type":"mixed","evidence_location":"mixed","evidence":"The localized anatomy is inconsistent and the surrounding texture also differs."}
	```

	Allowed evidence types are `none`, `local_medical`, `local_forensic`,
	`local_medical_and_local_forensic`, `global_forensic`, and `mixed`. Allowed
	locations are `none`, `local_bbox`, `global_or_outside`, and `mixed`.

	## Score predictions

	```bash
	git clone https://huggingface.co/datasets/leaf0932/CausalMedBench
	cd CausalMedBench
	python -m pip install -r requirements.txt

	python evaluation/score.py \
	--ordinary-predictions /path/to/ordinary_predictions.jsonl \
	--five-view-predictions /path/to/five_view_predictions.jsonl \
	--reasoning-predictions /path/to/reasoning_predictions.jsonl \
	--output metrics.json
	```

	The scorer reports:

	- ordinary Accuracy and Macro-F1, including modality, forgery-type, and
	generator breakdowns;
	- five-view accuracy and all-five-correct consistency;
	- local and global evidence sufficiency;
	- benign nuisance invariance and authentic recovery;
	- explanation verdict, evidence-type, evidence-location, and joint alignment.

	Run `python evaluation/score.py --help` for local dataset and strict-coverage
	options. A submission template is included in `examples/`.

	## Dataset fields

	Important fields include `record_id`, `group_id`, `view_id`, `target_label`,
	`image`, `bbox_normalized_1000`, `modality`, `forgery_type`, `generator`,
	`disease`, `operation`, `evidence_available`, `evidence_removed`, and
	`is_reasoning_subset`. Bounding boxes use a 0–1000 normalized coordinate system.

	The released masks are rectangular box rasterizations, not pixel-precise
	segmentation masks. The locally composed views use an 8-pixel feathered box
	boundary. Nuisance operations are deterministic and recorded per row.

	## Source and license

	CausalMedBench uses source data from
	[`RichardChenZH/MedForge-90K`](https://huggingface.co/datasets/RichardChenZH/MedForge-90K).
	The data, including redistributed and derived images, is released under the
	Computational Use of Data Agreement (C-UDA). Users must comply with C-UDA and
	preserve the upstream attribution. Benchmark code in `evaluation/` and `scripts/`
	is provided under Apache-2.0. See `LICENSE` and `DATA_LICENSE.md` for details.

	The MedForge citation acknowledges the upstream data source only. It does not
	attribute CausalMedBench, its five-view construction, its evaluation protocol,
	or experiments conducted with CausalMedBench to the MedForge authors.

	This benchmark is for research and educational use. It is not a clinical
	diagnostic tool and must not be used to make patient-care decisions.

	## Citation

	Please cite CausalMedBench for the benchmark, five-view construction, evaluation
	protocol, metrics, and scorer:

	```bibtex
	@misc{you2026causalmedbench,
	title={CausalMedBench: Evaluating Evidence Dependency and Explanation Faithfulness in Medical Deepfake Reasoning},
	author={You, Maowei},
	year={2026},
	howpublished={Hugging Face dataset},
	url={https://huggingface.co/datasets/leaf0932/CausalMedBench},
	version={1.0.0}
	}
	```

	The manuscript citation will replace the repository citation after the paper is
	publicly archived. Because the released images and source annotations originate
	from MedForge-90K, also cite MedForge as the upstream data source:

	```bibtex
	@misc{chen2026medforge90k,
	title={MedForge-90K},
	author={Chen, Zhihui and He, Kai and Lei, Qingyuan and Pu, Bin and Zhang, Jian and Xu, Yuling and Feng, Mengling},
	year={2026},
	howpublished={Hugging Face dataset},
	url={https://huggingface.co/datasets/RichardChenZH/MedForge-90K},
	note={Upstream source dataset used to construct CausalMedBench}
	}
	```

	These two citations serve different purposes: the first credits CausalMedBench;
	the second preserves the required attribution for the MedForge-90K source data.