256 & 0.72 & NA & 47\
1x1 & 2048 & 1024 & 0.65 & NA & 43\

**VGG-based networks** \[vgg\_auc\] We then tested whole image classifiers using VGG16 as the patch classifier and VGG blocks as the top layers. BN was used for the VGG blocks on the top but not for the VGG16 patch classifier because it is a pre-trained network which cannot be modified. The VGG-based whole image classifiers performed similarly to the Resnet-based ones but took longer to achieve the same performance (Table \[tab:vgg\_auc\]). Using more convolutional layers in VGG blocks led to poorer performance: using two VGG blocks of $512 \times 1$ (AUC=0.83) performed better than two VGG blocks of $512 \times 3$ (AUC=0.81); using two VGG blocks of $256 \times 1$ and $128 \times 1$ (AUC=0.85) performed better than two VGG blocks of $256 \times 3$ and $128 \times 3$ (AUC=0.83). On the contrary to the Resnet, reducing the depths of the two VGG blocks from $512$ and $512$ to $256$ and $128$ increased the AUC. Reducing the depths further to $128$ and $64$ still yielded an AUC of 0.84. Fig. \[fig:vgg\_loss\] illustrates that the classifier with the two VGG blocks of $512 \times 