896$. Two patch image sets were created by sampling image patches from ROIs and background regions. All patches had the same size of $224 \times 224$. The first dataset (S1) was comprised of sets of patches in which one is centered on the ROI and one is a random background patch from the same image. The second dataset (S10) was derived from 10 sampled patches around each ROI with a minimum overlapping ratio of 0.9 and the same number of background patches from the same image. All patches were classified into one of the five categories: background, malignant mass, benign mass, malignant calcification and benign calcification.

### Network training

Training a whole image classifier was achieved in two steps. The first step was to train a patch classifier. We compared the networks with pre-trained weights using the ImageNet [@russakovsky_imagenet_2015] database to those with randomly initialized weights. For a pre-trained network, the bottom layers represent primitive features that tend to be preserved across different tasks, whereas the top layers represent higher-order features that are more related to specific tasks and require further training. Using the same learning rate for all layers may destroy the features that are already learned in the 