Fig. 8: Ten tissue type and three disease state prediction performance and counts. | Modern Pathology

Fig. 8: Ten tissue type and three disease state prediction performance and counts.

From: Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

Fig. 8

A Classifier performance for predicting histopathology tissue type (ten types, 8331 images). B Classifier performance for predicting disease state (three disease states; 6549 images). Overall AUROC is the weighted average of AUROC for each class, weighted by the instance count in the class. These panels (A, B) show AUROC (with ten-fold cross-validation) for the chosen classifier. Random Forest AUROC for tissue type prediction is 0.8133 (AUROC for the ten replicates: mean ± stdev of 0.8134 ± 0.0007). AUROC is 0.8085 for an ensemble of our deep-learning-Random-Forest hybrid classifiers for disease state prediction (AUROC for the ten replicates: mean ± stdev of 0.8035 ± 0.0043). C1 Disease state counts per tissue type. The proportion of nontumor vs. low-grade vs. malignant disease states varies as a function of tissue type. For example, dermatological tissue images on social media are most often low grade, but malignancy is most common for genitourinary images. C2 Disease state counts as a function of whether a marker test (e.g., IHC, FISH) was mentioned (˜25% of cases) or not. IHC is the most common marker discussed and is typically, but not necessarily, used to subtype malignancies.

Back to article page