SSANet: spatial stain attention network for pathological images classification

Histopathological images classification plays a significant role in cancer diagnosis, but current deep learning methods fail to account for the unique characteristics of histopathological images. To address this limitation, we present SSANet, a Spatial and Stain Attention Network focusing on staining information in the cell nucleus and cytoplasm of histopathological images. Our approach first separates the stain channels and generates a stain attention map using a specialized stain attention module, which then activates staining information in the feature map. The experiments on two computational pathology problems, CRC-HE and BreakHis datasets, demonstrate that our method outperforms state-of-the-art methods with test F1 values up to 97.63% and 94.91% for cancer subtypes classification. Our contributions include the novel SSANet architecture, a stain attention module that enhances the focus on crucial cytosolic and cytoplasmic information, and improved classification results for histopathological images.