A parasitic metric learning net for breast mass classification based on mammography

Accurate classification of different tumors in mammography plays a critical role in the early diagnosis of breast cancer. However, owing to variations in appearance, it is a challenging task to distinguish malignant instances from benign ones. For this purpose, we train a deep convolutional neural networks (CNNs) to obtain more discriminative description of breast tissues. Benefiting from the discriminative representation, metric learning layers are proposed to further improve performance of the deep structure. The best-performing model restricts the depth of backpropagation of joint training in only the metric learning layers. Relation between metric learning layers and tradition CNNs structures seems like parasitism relationship between species, where one species, the parasite, benefits at the expense of the other. Therefore, the proposed method is named as parasitic metric learning net. To confirm veracity of our method, classification experiments on breast mass images of two widely used databases are performed. Comparing performance of the proposed method with traditional ones, competitive results are achieved. Meanwhile, the parameter updating strategy for our parasitic metric net may inspire a way of improving performance of a pre-trained CNNs model on particular medical image processing or other computer vision tasks.