Utilizing State Space Model for Diffusion Processes in Breast Tumor Pathological Image Super-Resolution

Breast tumor pathological images play a crucial role in diagnosing and treating breast cancer. However, due to limitations in imaging technology, the resolution of pathological images varies across different magnification levels, affecting the accuracy of diagnosis. This study proposes a novel approach to address this issue by leveraging state space modeling for diffusion processes. Our method first utilizes Scale-Invariant Feature Transform for feature registration to achieve spatial alignment of pathological images at different focal depths for super-resolution training. Subsequently, we employ a diffusion process integrated with a state space model for pathological image super-resolution. We elucidate the advantages of the diffusion model, particularly its effectiveness in generating high-resolution images, thus enhancing super-resolution capabilities. Finally, we discuss the advantages of the state space model and its pivotal role in our approach.