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  Indian J Med Microbiol
 

Figure 3: This chart depicts the overall study design. First, each of the three datasets are individually used to create the training sets. Second, each model is tested internally against the aforementioned withheld randomly selected test set to assess the models' internal validation accuracy with a 10 k-fold random sampling cross validation approach. Third, each model is tested externally against both of the other datasets to assess each model's performance, and the results are averaged across the ten models (another 10 k-fold cross validation). Then, each test is repeated with a “top n” correct criteria of one, three, and five which represents how each model performs in identifying the top 1, 3 or 5 differential diagnosis (top look-alikes) within each histologic category. Additionally, two combined datasets are generated from the three individual data sources (University of California, Davis, New York University, external data), one with restricted data quantity, and one with full data quantity. Once again, these datasets are resampled to train combination models along with 10 k-fold cross validation. Finally, all of the models, including both combination sets and all three individual datasets, were tested against a generalization test set (google images) obtained from online public domain images

Figure 3: This chart depicts the overall study design. First, each of the three datasets are individually used to create the training sets. Second, each model is tested internally against the aforementioned withheld randomly selected test set to assess the models' internal validation accuracy with a 10 k-fold random sampling cross validation approach. Third, each model is tested externally against both of the other datasets to assess each model's performance, and the results are averaged across the ten models (another 10 k-fold cross validation). Then, each test is repeated with a “top <i>n</i>” correct criteria of one, three, and five which represents how each model performs in identifying the top 1, 3 or 5 differential diagnosis (top look-alikes) within each histologic category. Additionally, two combined datasets are generated from the three individual data sources (University of California, Davis, New York University, external data), one with restricted data quantity, and one with full data quantity. Once again, these datasets are resampled to train combination models along with 10 k-fold cross validation. Finally, all of the models, including both combination sets and all three individual datasets, were tested against a generalization test set (google images) obtained from online public domain images