Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging toward digital pathology

Lensless fiber endomicroscopy is an emerging tool for minimally invasive in vivo imaging, where quantitative phase imaging can be utilized as a label-free modality to enhance image contrast.Nevertheless, current phase reconstruction techniques in lensless multi-core fiber endomicroscopy are effective for simple structures but face significant challenges with complex tissue imaging, thereby restricting their clinical applicability.We present SpecDiffusion, a speckle-conditioned diffusion model tailored to achieve highresolution and accurate reconstruction of complex phase images in lensless fiber endomicroscopy.Through an iterative refinement of speckle data, SpecDiffusion effectively reconstructs structural details, enabling highresolution and high-fidelity quantitative phase imaging.This capability is particularly advantageous for digital pathology applications, such as cell segmentation, where precise and reliable imaging is essential for accurate cancer diagnosis and classification.New perspectives are opened for early and accurate cancer detection using minimally invasive endomicroscopy.