Fig. 2: Summary of performance metrics by cancer type.

a Pie chart showing the distribution of studies by most investigated cancer types. b Number of studies published per year for the most investigated cancer types. c Number of papers utilizing various publicly available datasets based on the number of publications. d Heatmap of the distribution of clinical tasks across different cancer types where the number of papers is normalized to the total number of studies per cancer type. e-h Box plots of various performance metrics by cancer type based on AUC for all data collection techniques (e), AUC for H&E data (f), average performance for H&E data only (g), and average performance for all data collection techniques (h). i Heatmap showing the median value of various performance metrics for each cancer type. The total number of papers included in the heatmap for each cancer type is shown above the heatmap. j Distribution of papers by machine learning tasks. k Box plot of model performance based on AUC across various machine learning tasks. AUC Area-Under-Curve, PPV Positive Predictive Value, NPV Negative Predictive Value.