VS-FPM: Large-Format, Label-Free Virtual Histopathology Microscopy.

ABSTRACT: Objective: This article describes a new method (VS-FPM) for analysis of unstained tissues based on the application of supervised machine learning to generate brightfield hematoxylin and eosin (H&E) images from phase images recovered using Fourier ptychographic microscopy (FPM). Impact Statement: VS-FPM has several advantages for label-free digital pathology. Capture of complex image information simplifies model training and allows post-capture refocusing. FPM images combine high resolution with a large field of view, and the hardware is low-cost and compatible with many existing brightfield microscope systems. Introduction: By generating realistic histologically stained images from label-free image data, virtual staining (VS) methods have the potential to streamline cl

SUBMITTER: Bendkowski C 

PROVIDER: S-EPMC12669476 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature