3$ suffered from overfitting: as the training loss decreased, the validation loss fluctuated wildly, while the classifier with the two VGG blocks of $512 \times 1$ showed smoother loss curves. Therefore, controlling model complexity (\#layers and depths) is important for achieving good performance with the VGG-based networks.

[@llllll@]{} **Patch set** & **Block1** & **Block2** & **AUC** & **A-AUC** & **\#Epochs**\
S10 & 512 x 3 & 512 x 3 & 0.81 & 0.82 & 91\
S10 & 512 x 1 & 512 x 1 & 0.83 & 0.86 & 98\
S10 & 256 x 3 & 128 x 3 & 0.83 & 0.85 & 51\
**S10** & **256 x 1** & **128 x 1** & **0.85** & **0.86** & **61**\
S10 & 128 x 1 & 64 x 1 & 0.84 & 0.85 & 142\
**S10** & **[\[]{}512-512-1024[\]]{} x 2** & **[\[]{}512-512-1024[\]]{} x 2** & **0.85** & **0.88** & **165**\
\
**Pool size** & **FC1** & **FC2** & **** & **** & ****\
5x5 & 64 & 32 & 0.71 & NA & 26\
2x2 & 512 & 256 & 0.68 & NA & 27\
1x1 & 2048 & 1024 & 0.69 & NA & 50\

![Train and validation loss 