Extended Data Fig. 6: Effect of the programmatic modification of image chromaticity on the predictions of the AI classifier. | Nature Biomedical Engineering

Extended Data Fig. 6: Effect of the programmatic modification of image chromaticity on the predictions of the AI classifier.

From: Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians

Extended Data Fig. 6

We separately applied three methods of image chromaticity modification (see Supplementary Methods), then calculated the mean change in AI classifier output relative to the original, unaltered images. Each method of chromaticity modification reflects the chromatic adaptation transform (white balancing method) provided by the corresponding color appearance model (CIE 1976 L* u* v*, CIE 1976 L* a* b*, or CAM16). To facilitate visualization, the vertical axis is normalized to the maximum absolute change in AI classifier output observed for a given method; the normalization factors are displayed at bottom right. Images indicate the effect of each given chromaticity modification. Color bars indicate the hue to which a neutral color (white) is shifted by the chromaticity modification; colorfulness in the color bar (but not example images) is exaggerated for ease of viewing.

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