Fig. 1: Impact of label granularity on model performance as a function of AUC. | npj Digital Medicine

Fig. 1: Impact of label granularity on model performance as a function of AUC.

From: High performance with fewer labels using semi-weakly supervised learning for pulmonary embolism diagnosis

Fig. 1

The graphs illustrate the performance of the models in terms of AUC across different datasets (a: RSPECT private test, b: external validation) as a function of the percentage of slice-level labels used. The solid lines represent the performance of average predictions across fivefold cross-validation, and the shaded areas correspond to the 95% confidence intervals (CI).

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