Feature fusion adversarial learning network for liver lesion classification

The number of training data is the key bottleneck in achieving good results for medical image analysis and especially in deep learning. Due to small medical training data, deep learning models often fail to mine useful features and have serious over-fitting problems. In this paper, we propose a clean and effective feature fusion adversarial learning network to mine useful features and relieve over-fitting problems. Firstly, we train a fully convolution autoencoder network with unsupervised learning to mine useful feature maps from our liver lesion data. Secondly, these feature maps will be transferred to our adversarial SENet network for liver lesion classification. Our experiments on liver lesion classification in CT show an average accuracy as 85.47% compared with the baseline training scheme, which demonstrate our proposed method can mime useful features and relieve over-fitting problem. It can assist physicians in the early detection and treatment of liver lesions.