Fig. 4: Performance of BioNet and competing methods evaluated by Classification Accuracy and Area Under the Curves (AUCs). | npj Digital Medicine

Fig. 4: Performance of BioNet and competing methods evaluated by Classification Accuracy and Area Under the Curves (AUCs).

From: Biologically informed deep neural networks provide quantitative assessment of intratumoral heterogeneity in post treatment glioblastoma

Fig. 4

Results are shown for developmental cohort A under leave-one-patient-out cross-validation (LOPO CV) in a, and for test cohort B in b. c A table summarizes the key performance metrics of BioNet and competing methods. The standard deviation of accuracy for cohort A, calculated using leave-one-patient-out cross-validation (LOPO CV), is shown in brackets.

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