Breast cancer is a highly heterogeneous malignant tumor, and its accurate classification is of great significance for clinical diagnosis and treatment decision-making. In recent years, convolutional neural networks and Transformer have been widely used in pathological image analysis of breast cancer. Though the former excels at capturing local information and the latter is adept at modeling global dependencies, the former is limited by fixed sampling positions and is hindered to characterize irregular cell morphology, the latter is insufficient in describing the two-dimensional spatial structure of cells and tissues. Based on these, a Spatial-Frequency Domain Feature Extraction Model (S-FDFEM) proposed in this paper integrates spatial and frequency domain information to enhance feature learning for pathological image recognition. Specifically, in the spatial domain, Deformable Bottleneck Convolution (DBottConv) is utilized to effectively represent intricate cell and tissue morphological variations in pathological images and improve the expression ability of local features; in the frequency domain, wavelet low frequencies and Fourier high frequencies are generated based on the input pathological images to capture global approximation and fine structures, then statistical transformer and depth gradient feature extraction modules are integrated to operate on these two frequency domain components, enabling global dynamic focusing and two dimensions spatial characterization of pathological images. Experimental results show that the classification of breast cancer pathological image on BreakHis and BACH datasets verify the superiority of the S-FDFEM proposed in this paper.
He et al. (Tue,) studied this question.