\[tab:resnet\_auc\]), which was lower than that of the same classifier without the heatmap. To exclude the possibility of overfitting due to the small depth of the heatmap, another model with reduced complexity using two Resnet blocks of $[64-64-256] \times 2$ and $[128-128-512] \times 2$ was tested, which achieved an AUC of 0.81. We conclude that removing the heatmap is beneficial to the all convolutional networks.

**The two-step approach** \[two\_step\_auc\] Finally, for comparison we tested a previously reported approach [@shen_breast_2017; @wang_deep_2016] that set a cutoff to binarize the heatmap; extracted regional features (such as area, major axis length and mean intensity) from the binary heatmap; and trained a random forest classifier (\#trees=500, max depth=9, min samples split=300) on the regional features. The Resnet50 patch classifier was used and the softmax activation was used in the heatmap to obtain the probabilities for the five classes. Four cutoffs—0.3, 0.5, 0.7 and 0.9 were used to binarize the heatmap and the regional features were combined. This approach achieved an AUC of 0.73, falling short of the all convolutional models.

Transfer learning for whole image classification on INbreast {#inbreast}
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### Setup and processing of the dataset

The INbreast [@moreira_inbreast_2012] dataset is a more recent 