FPM2Stain Net: physics-guided super-resolution and multi-modal virtual staining for digital histopathology.

ABSTRACT: We propose FPM2Stain Net, a jointly optimized end-to-end computational pipeline that integrates physics-guided super-resolution with deep learning-based virtual staining to enable high-resolution, multi-modal digital histopathology. The first stage, bidirectional physics-based Fourier ptychographic microscopy (BiP-FPM), reconstructs high-resolution amplitude and phase maps from a multi-LED FPM image stack using a self-supervised ResNet-U-Net guided by a differentiable point spread function (PSF) model with learnable pupil correction and bidirectional physics-consistent constraints. The second stage employs a multi-task conditional generative adversarial network (cGAN) enhanced with wavelet-based spatial-frequency fusion and perceptual supervision to synthesize clinically relevant staining

SUBMITTER: Yang Q 

PROVIDER: S-EPMC12904525 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature