Pathology Image Restoration via Mixture of Prompts
Journal:
arXiv
Published Date:
Mar 16, 2025
Abstract
In digital pathology, acquiring all-in-focus images is essential to
high-quality imaging and high-efficient clinical workflow. Traditional scanners
achieve this by scanning at multiple focal planes of varying depths and then
merging them, which is relatively slow and often struggles with complex tissue
defocus. Recent prevailing image restoration technique provides a means to
restore high-quality pathology images from scans of single focal planes.
However, existing image restoration methods are inadequate, due to intricate
defocus patterns in pathology images and their domain-specific semantic
complexities. In this work, we devise a two-stage restoration solution
cascading a transformer and a diffusion model, to benefit from their powers in
preserving image fidelity and perceptual quality, respectively. We particularly
propose a novel mixture of prompts for the two-stage solution. Given initial
prompt that models defocus in microscopic imaging, we design two prompts that
describe the high-level image semantics from pathology foundation model and the
fine-grained tissue structures via edge extraction. We demonstrate that, by
feeding the prompt mixture to our method, we can restore high-quality pathology
images from single-focal-plane scans, implying high potentials of the mixture
of prompts to clinical usage. Code will be publicly available at
https://github.com/caijd2000/MoP.
