is reported in Table \[tab:patch\_acc\]. The S10 set is more difficult to classify than the S1 set because the former contains the ROIs’ neighboring and background regions that are challenging to distinguish. On the S1 set, both randomly initialized and pre-trained Resnet50 classifiers achieved similar accuracy but the pre-trained network converged after half as many epochs as the randomly initialized one. On the S10 set, the pre-trained Resnet50 outperformed the randomly initialized one by a large margin, achieving an accuracy of 0.89. These results showed that pre-training can greatly help network convergence and performance. Therefore, pre-trained networks were used for the rest of the study. The pre-trained VGG16 achieved an accuracy of 0.84 on the S10 set, falling short of the pre-trained Resnet50’s performance.

  **Model**   **Pretrained**   **Patch set**   **Accuracy**   **\#Epochs**
  ----------- ---------------- --------------- -------------- --------------
  Resnet50    N                S1              0.97           198
  Resnet50    Y                S1              0.99           99
  Resnet50    N                S10             0.63           24
  Resnet50    Y                S10             0.89           39
  VGG16       Y                S10             0.84           25

  : Test accuracy of the patch classifiers using the Resnet50 and VGG16. \#Epochs indicates the epoch when the best validation accuracy has been reached.[]{data-label="tab:patch_acc"}

### Converting patch to whole image classifiers

Using pre-trained Resnet50 and VGG16 as patch classifiers, we 