curves of two VGG structures.[]{data-label="fig:vgg_loss"}](vgg_loss_curves_scaled_300.jpg){width="0.7\linewidth"}

**Hybrid networks** \[hybrid\_auc\] We also created two hybrid networks by adding the best VGG top layers - two VGG blocks of $256 \times 1$ and $128 \times 1$ - on top of the Resnet50 patch classifier; and the best Resnet top layers - two Resnet blocks of the same configuration of $[512-512-1024] \times 2$ on top of the VGG16 patch classifier. The two hybrid networks achieved AUC of 0.87 and 0.85, respectively; they are among the best performing models (Tables \[tab:resnet\_auc\] & \[tab:vgg\_auc\]).

**Augmented prediction and model averaging** \[augmented\_ensemble\] Augmented prediction was implemented by horizontally and vertically flipping an image to obtain four images and taking an average of the four images’ scores. This technique increased the AUC (referred to as A-AUC) for each model by 0.01-0.03 (Tables \[tab:resnet\_auc\] & \[tab:vgg\_auc\]). The four best performing models were chosen to make a model ensemble by taking an average of their augmented prediction scores: two models used Resnet50 and VGG16 as patch classifiers and Resnet and VGG blocks as top layers, respectively (referred to as Resnet-Resnet and VGG-VGG); and two hybrid models (referred to as Resnet-VGG and VGG-Resnet). Fig. \[fig:roc\_curve\]a shows the Receiver Operating Characteristic (ROC) curves 