## 2 Background  
### Deep Feature Learning in Images  
In recent years, deep learning approaches have been widely used in several applications, especially for classification problems. A convolutional neural network (CNN) is one of the most popular deep learning models that is typically composed of two main parts; feature learning (i.e., convolution and pooling layer) and classification (i.e., fully connected layer) [18; 19; 20; 21; 26; 27; 28; 29]. Several papers on breast cancer detection and diagnosis have been published; for example, the stacked sparse autoencoder (SSAE) algorithm was proposed by [30] to analyze breast cancer histopathology images. In the training process, they adopted a greedy strategy to optimize the SSAE, trained each hidden layer, and then fed the output to the next layer. In addition, a model that pretrained on ImageNet and built from several images for mitosis classification was proposed by [31]. In this work, three fully connected layer configurations were also presented to separately improve their robustness. Finally, multiple probabilities were obtained by the training process and averaged as the final output. Another model, a deep learning-based framework to discover and locate unusual areas in a mammogram was proposed by [28]. A segmentation algorithm that combines different types of probabilistic models was proposed by [32]. The algorithm adopts a structured SVM that considers prior locations, a deep belief network, and a Gaussian mixture model for mammographic image segmentation. Furthermore, ref. [29] explored a classification model utilizing DeCaf features (also known as neural codes) for breast histopathological images by applying AlexNet [19] to build a deep learning network called CaffeNet. The outputs from the network layers of CaffeNet were used as DeCaf features for further classification. Besides, a deep learning approach based on crowd annotations (called AGCNet) was presented by [26], this model combines a multiscale convolutional neural network (CNN) architecture with an aggregation layer (AL) to directly handle data aggregation. To cope with insufficient of training images [27] developed a transfer learning approach for a deep CNN (DCNN) model. This learning model was trained for the mass segmentation of mammographic images. Moreover, ref. [19] presented a pretrained AlexNet network that was tuned by a fine-tuning strategy that used images from the ImageNet database [33] for training, and a deep learning model relying on a convolutional architecture called a convolutional sparse autoencoder (CSAE) was proposed by [34]. The model aims to solve the pixelwise labeling problem and to extract more abstract features from unlabeled data derived from mammogram images. To learn the latent bilateral features from the 3D volume data of digital breast tomosynthesis (DBT) [35] presented a self-taught deep learning model by using a 3-D multiview DCNN. The volumes of interest (VOIs) of the source and target are first transformed by the geometric VOI transformation function before feeding them to a DCNN model. Then, high-level latent bilateral features are obtained by those DCNN structures. Another DCNN was proposed by [36] to classify three classes of mammogram images (normal, benign, and malignant). First, the region of interest (ROI) is determined to enhance the image contrast. Then, the CNN is trained on three image groups: the original ROI images, the images reproduced by a nonnegative matrix factorization (NMF) function, and the images reproduced by a statistical self-similarity function. These functions are used to cope with overfitting and to achieve increased classification accuracy. The concept of a multiple-instance learning (MIL)framework was proposed by [37] to investigate the affinity of MIL, which relies on the analysis of histopathology images for a breast cancer patient diagnosis case without the need for labelling all instances. In addition, ref.