Fig. 4 | BMC Bioinformatics

Fig. 4

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

Fig. 4

Detection and visualization of histopathologic outliers using t-SNE. a-b t-SNE-based WSI visualization and classification of a gliosarcoma (rare glioma subtype) (a) and a hemangioblastoma (b). Unlike previous examples, these lesions represent patterns and tumor types never previously encountered by the CNN. Localization of the vast majority of lesional tiles within the unoccupied space allows confident visual and statistical classification as an “outlier” without the need for a reference ROC curve. Insets (lower right) magnify the localization of tiles in unoccupied space. These examples demonstrate how the properties of the t-SNE plot can be leveraged to detect erroneous classification of novel/challenging cases. c. ROC performance summary on the same set of test WSIs used in Fig. 2. Classification using t-SNE tile distributions yields a similar performance (AUC) metric to the probability score-based approach. d relationship of t-SNE accuracy at different defined “outlier” cutoffs for comparison. Although more conservative in WSI classification, this t-SNE approach shows a more uniform performance (orange; error rate) across different “cutoff scores”. This distinct feature improves its generalizability when cut-off values cannot be reliably or empirically estimated

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