Fig. 4

Testing tile-level histology classification performance of the rbfSVM classifier with the Emory Hospital development cohort. Each bar represents the weighted average of tiles and their predicted probabilities during testing for a histology class. Specificity (green) measures how often the rbfSVM classifier correctly predicted true negatives. AUC (blue) reflects the model's ability to distinguish between positive and negative classes. Accuracy (yellow) indicates the proportion of correct predictions out of total predictions. F1-score (gray) presents a balanced view of rbfSVM classifier performance. Sensitivity (orange) suggests how often the rbfSVM classifier correctly identifies positive instances. Precision (blue) indicates how often the rbfSVM classifier correctly predicts true positives. Error bars represent the 95% confidence interval in each case