Fig. 1: System overview. | Nature Communications

Fig. 1: System overview.

From: Accurate auto-labeling of chest X-ray images based on quantitative similarity to an explainable AI model

Fig. 1

Standardized, automated labeling method, based on similarity to a previously validated five-label chest X-ray (CXR) detection explainable AI (xAI) model, using an xAI model-derived-atlas based approach. a Our quantitative model-derived atlas-based explainable AI system calculates a probability-of-similarity (pSim) value for automated labeling, based on the harmonic mean between the patch similarity and the confidence. The resulting pSim metric can be applied to a “mode selection” algorithm, to either label the external input images to a selected threshold-of-confidence, or alert the user that the pSim value falls below this selected threshold. b The model-derived atlas-based method calculates patch similarity and confidence, based on class activation mapping (CAM)38,39 and predicted probability from the model, for each clinical output label. c The harmonic mean between the patch similarity and confidence is then used to calculate a pSim for each clinical output label in mode selection.

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