Fig. 2.

SCNN uses image sampling and filtering to improve the robustness of training and prediction. (A) During training, a single 256 × 256-pixel HPF is sampled from each region, producing multiple HPFs per patient. Each HPF is subjected to a series of random transformations and is then used as an independent sample to update the network weights. New HPFs are sampled at each training epoch (one training pass through all patients). (B) When predicting the outcome of a newly diagnosed patient, nine HPFs are sampled from each ROI, and a risk is predicted for each field. The median HPF risk is calculated in each region, these median risks are then sorted, and the second highest value is selected as the patient risk. This sampling and filtering framework was designed to deal with tissue heterogeneity by emulating manual histologic evaluation, where prognostication is typically based on the most malignant region observed within a heterogeneous sample. Predictions based on the highest risk and the second highest risk had equal performance on average in our experiments, but the maximum risk produced some outliers with poor prediction accuracy.