Cascaded and Dual: Discrimination Oriented Network for Brain Tumor Classification
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:363-378, 2019.
Medical image classification is one of the fundamental research topics in the domain of computer-aided diagnosis. Although existing classification models of the natural image can produce promising results using deep convolutional neural networks in some cases, it is difficult to guarantee that these models can generate promising performance for medical images. To bridge such a gap, we propose a novel medical image classification method for brain tumors in this paper, termed as Discrimination Oriented Network (DONet). Inspired by the attention learning mechanism of the human brain, we first propose two categories of attention learning modules, i.e., the Cascaded Attention Learning (CAL) and the Dual Attention Learning (DAL), which can learn the discrimination information in both the spatial-wise and the channel-wise dimensions in a fine-grained manner. By the CAL and the DAL, the attention information of different dimensions is calculated in a series manner (for cascaded) and a parallel manner (for dual), respectively. To demonstrate the superiority of our proposed modules, we implement the CAL and the DAL on the Deep Residual Network (ResNet) for brain tumor classification. Compared with the ResNet, experimental results show that the DONet has a significant improvement in accuracy. Moreover, compared with state-of-the-art classification methods, the DONet can also achieve better performance.