SSSNet: Small-Scale-Aware Siamese Network for Gastric Cancer Detection

In recent years, deep neural networks have become the most powerful supervised learning method. Several advanced neural networks, such as AlexNet, ZFNet, Inception, ResNet, and DenseNet, have achieved excellent performance on image recognition tasks. However, deep neural networks rely heavily on huge training sets to obtain good performance. Many applications, such as medical image analysis, do not allow for such large training sets, and it is difficult to train such networks on small-scale training sets. Magnifying narrow band imaging (M-NBI) is widely used to assist doctors in diagnosing gastric cancer, but relatively few of these images are available, compared with the number of general images. In this paper, we propose to use a Siamese network architecture to learn discriminative feature representations based on pairs of images. Then, we use a micro neural network to recognize these features and classify the input images. Our experimental results show that the proposed network can effectively learn discriminative features from a limited number of training images, and also that it can successfully recognize gastric cancer in M-NBI images.