Fig. 2 | BMC Bioinformatics

Fig. 2

From: Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction

Fig. 2

Probability score-based classification workflow and performance. a Automated lesion segmentation and classification workflow for 180 prospective and randomly selected WSIs of cerebral lesions. Only image tiles with a lesional probability score of > 85% were used for class predictions. To reduce noise, classification was only carried out on WSIs with > 15 lesional tiles (n = 147). The majority of unclassified WSIs (n = 33) represented non-neoplastic processes (e.g. epidermoid cysts, hemorrhage, normal brain tissue). b Multi-class ROC curves were empirically generated by deriving the sensitivity (fraction of detected true positives) and specificity (fraction of detected true negatives) at different probability score distribution thresholds. The displayed AUC is a measure of performance with a minimum value of 0.50 (random predictions) and 1.0 (all correct predictions). c Relationship of the accuracy of the top classification output at different minimum probability score cutoffs. If this cutoff value is not reach, the case is deemed “undefined” and not included in the scoring. This empirical post-hoc analysis highlights a specific threshold where the error rate substantially rises. d A H&E-stained validation WSI of a gliosarcoma (glioma subtype), confirmatory special stains and the CAM showing the top CNN probability score-based prediction. In this study, we define these misclassification between lesion types as Type B errors. Black scale bar represents 4 mm. e An example of an erroneously classified tumor type (hemangioblastoma) that was not included in this 13-class model (“Type C error”). Black scale bar represents 3 mm

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