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rai-check-dl

Deep learning audits for image classification, medical imaging, and scientific AI.

Features

  • Image classification accuracy and per-class recall checks
  • Robustness checks for recorded or callback-generated transformation predictions
  • Built-in brightness, contrast, horizontal-flip, and Gaussian-noise transformations
  • Grad-CAM heatmaps with PyTorch hooks or TensorFlow GradientTape
  • Medical imaging checks for patient leakage across splits and site-level accuracy bias
  • Segmentation Dice, IoU, mask-quality, and subgroup checks
  • Object-detection mAP, IoU-threshold, class-coverage, and subgroup checks
  • DICOM metadata, scanner/protocol bias, near-duplicate leakage, calibration, and patient aggregation
  • Grad-CAM transformation stability and optional localization-overlap checks

Python API

from rai_audit.dl import ImageClassificationAudit

report = ImageClassificationAudit(
    y_true=y_true,
    y_pred=y_pred,
    transformed_predictions={"sensor_noise": noisy_predictions},
    persist=False,
).run()

To evaluate transformations directly, pass images and a predictor callback.

CLI

Audit recorded predictions from CSV:

rai-check dl run --data predictions.csv --task image --format html

Medical imaging CSV audits can add --patient-id, --split, and --site.

Examples

Metadata

Release files for rai-check-dl 0.0.2

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