datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
AMAZON_FFR_TVTffr-anatomy-prediction-discordance-detection-v0.1Goal
Detect discordancebetween coronary anatomy complexityand AI-derived FFR accuracyagainst invasive FFR ground truth.
This targets silent degradationin specific patient subgroups.
Inputs
vessel_tortuosity
calcification_burden
lesion_length_mm
segmentation_confidence
image_artifact_score
ai_ffr_prediction
ai_ffr_run_variance
model_disagreement
invasive_ffr_ground_truth
Required outputs
discordance_flag
discordance_type
subgroup_risk_label
reliability_drop_score
Discordance types
Examples:… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-anatomy-prediction-discordance-detection-v0.1.FFR
[!NOTE]
Dataset origin: https://github.com/bonaventuredossou/ffr-v1
Description
The authors of the dataset provide a description in the following PDFs: here and here.
Citation
@inproceedings{emezue-dossou-2020-ffr,
title = "{FFR} v1.1: {F}on-{F}rench Neural Machine Translation",
author = "Emezue, Chris Chinenye and
Dossou, Femi Pancrace Bonaventure",
editor = "Cunha, Rossana and
Shaikh, Samira and
Varis, Erika and
Georgi, Ryan… See the full description on the dataset page: https://huggingface.co/datasets/FrancophonIA/FFR.ffr-physiology-prediction-coherence-baseline-mapping-v0.1
Goal
Define the baseline coherencebetween AI-derived FFR predictionsand real physiological signals.
Signals include:
myocardial perfusion
wall motion
stress test results
vital signs
This dataset establisheswhat physiologically plausible alignmentlooks like.
Without this baselineimplausibility cannot be detected.
Required output
The model must provide:
physiological_coherence_score
interpretation of alignment error
baseline_label
Why this matters… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-physiology-prediction-coherence-baseline-mapping-v0.1.ffr-physiological-plausibility-decay-detection-v0.1Goal
Detect when an AI-derived FFR valuebecomes physiologically implausiblegiven other modalities.
The warning signal is coherence loss.Not a single bad threshold.
Inputs
ai_ffr_prediction
myocardial_perfusion_index
wall_motion_score
stress_test_result
vital signs (heart rate, blood pressure)
physiological_coherence_score
expected plausibility band
Required outputs
plausibility_decay_flag
decay_type
modality_conflict_label
plausibility_drop_score
Decay types
Examples:… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-physiological-plausibility-decay-detection-v0.1.ffr_charts
🕹️ FFR Charts Dataset
Description
Revisions
Dataset Distribution
How to download and use FineVideo
Using datasets
Using huggingface_hub
Load a subset of the dataset
Dataset Structure
Data Instances
Data Fields
Dataset Creation
License CC-By
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Additional Information
Credits
Future Work
Opting out of FineVideo
Citation Information
Terms of use for FineVideo
Description… See the full description on the dataset page: https://huggingface.co/datasets/stepmanai/ffr_charts.ffr-center-protocol-coherence-baseline-mapping-v0.1Goal
Monitor site-specific protocol coherencefor AI-derived FFR systems.
This dataset maps whether a hospitalis still operating insidethe model’s validated acquisition envelope.
Inputs
scanner vendor and model
reconstruction kernel
slice thickness
heart rate control approach
contrast protocol signature
image quality proxies
motion artifact rate
signal to noise
Required outputs
site_coherence_score
protocol_deviation_index
risk_flag
Interpretation
site_coherence_scorehow aligned this… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-center-protocol-coherence-baseline-mapping-v0.1.ffr-center-performance-drift-detection-v0.1Goal
Detect center-specific performance driftbefore audit failure.
This dataset measures coherence decaybetween a site’s acquisition protocoland the model’s known performance baseline.
Inputs
Site window metrics:
protocol signature hash
motion artifact rate
signal to noise
plausibility conflict rate
rolling AUC and MAE
calibration error shift
coherence trend
Required outputs
drift_type
predicted_failure_risk
detection_confidence
Drift types
Examples:
none
minor protocol shift
protocol… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-center-performance-drift-detection-v0.1.ffr-center-failure-surface-routing-v0.1Goal
When a center driftsroute the safest operational response.
This dataset treats each siteas a failure surface.
Not a single bad scan.
Inputs
Rolling site window metrics:
image quality proxies
protocol signature
rolling performance (AUC, MAE)
calibration error shift
plausibility conflict rate
drift type and risk
Required outputs
failure_surface_type
subgroup_affected
intervention_route
recalibration_needed
protocol_reset_plan
escalation_priority
confidence_score
Failure surfaces… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-center-failure-surface-routing-v0.1.ffr-anatomy-prediction-coherence-baseline-mapping-v0.1
Goal
Define the baseline coherencebetween coronary anatomy complexityand AI-derived FFR predictions.
This dataset establisheswhere anatomy and predictionnormally align.
Without this baselinediscordance cannot be detected.
Inputs
vessel tortuosity
calcification burden
lesion length
segmentation confidence
AI FFR prediction
invasive FFR ground truth
Required output
The model must state:
coherence_score
prediction_error interpretation… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ffr-anatomy-prediction-coherence-baseline-mapping-v0.1.