Y-diff: Structure-Texture Decoupled Diffusion Distillation for H&E-to-pCLE Translation

Probe-based Confocal Laser Endomicroscopy (pCLE) enables real-time in vivo optical biopsies. However, acquiring large-scale, high-quality pCLE images is prohibitively expensive and technically complex, causing severe training data scarcity. Furthermore, when applied to pCLE image generation, existing cross-modal translation models consistently introduce artifacts and cellular structure distortions. To address this, we propose Y-diff, a novel generative framework translating widely accessible H&E-stained pathology images into the scarce pCLE modality with high fidelity, providing robust data support for computational pathology. Y-diff innovatively introduces a knowledge distillation mechanism into this task paradigm, efficiently decoupling the learning of pCLE-domain textures from that of H&E pathology staining domain spatial structures. Specifically, a Teacher model captures distinct pCLE optical textures via color encoding, while a Student model extracts and distills this knowledge under strict H&E spatial constraints. Extensive experiments demonstrate that Y-diff consistently outperforms baselines, while significantly alleviating conventional translation artifacts and structural distortions. What’s more, the synthesized data further expands the scale of this rare modality, reducing mean absolute percentage error to 13.84% in downstream cell counting tasks. Source code is available at: https://github.com/Hdw2agon/Y_diff.