Medical Frequency Domain Learning: Consider Inter-class and Intra-class Frequency for Medical Image Segmentation and Classification

Medical image segmentation and classification tasks have become increasing accurate by employing deep neural networks. However, existing convolution neural networks models (CNNs) are challenging to achieve quite satisfactory results as medical objects and backgrounds are usually indistinguishable in spatial-domain images. In comparison, it is easier to analyze complex objects in frequency-domain images as different object information is retained in different frequency components. However, training CNNs in the frequency domain requires complex modification for network architecture. Thus, this paper proposes a method of learning in the frequency domain to train CNNs called Frequency domain attention (FDAM) Workflow, which only requires little parameters rise and modification in CNNs. FDAM utilizes the relationship of intra-class frequency to retain valuable frequency information and suppress trivial ones. Furthermore, to reduce computation, a Gate module is designed for deleting redundant frequency channels by exploiting the relationship of inter-class frequency. The proposed methods can be applied in various CNNs, such as U-Net, ResNet and DenseNet, while accepting frequency-domain data as input. Experiment results show a significant performance improvement compared to original CNNs for retinal vessel segmentation, glaucoma classification and pneumonia classification. Specifically, Gate module can improve accuracy while using less input data size.