MiHATP:A Multi-Hybrid Attention Super-Resolution Network for Pathological Image Based on Transformation Pool Contrastive Learning

Digital pathology slides can serve medical practitioners or aid in computer-assisted diagnosis and treatment. Collection personnel typically employ hyperspectral microscopes to scan pathology slides into Whole Slide Images (WSI) with pixel counts reaching the million level. However, this process incurs significant acquisition time and data storage costs. Utilizing super-resolution imaging techniques to enhance low-resolution pathological images enables downstream analysis of pathological tissue slice data under low-resource and cost-effective medical conditions. Nevertheless, existing super-resolution methods cannot integrate attention information containing variable receptive fields and effective means to handle distortions and artifacts in the output data. This leads to differences between super-resolution images and authentic images depicting cell contours and tissue morphology. We propose a method named MiHATP: A Multi(Mi)-Hybrid(H) Attention(A) Network Based on Transformation(T) Pool(P) Contrastive Learning to address these challenges. By constructing contrastive losses through reversible image transformation and irreversible low-quality image transformation, MiHATP effectively reduces distortion in super-resolution pathological images. Additionally, within MiHATP, we design a Multi-Hybrid Attention structure to ensure strong modeling capability for long-distance and short-distance information, thereby ensuring that the super-resolution network can obtain richer image information. Experimental results demonstrate superior performance compared to existing methods. Furthermore, we conduct tests on the output images of the super-resolution network for downstream cell segmentation and phenotypes tasks, achieving performance similar to that of original high-resolution images.