Enhancing classification of rare white blood cells in FPM with a physics-inspired GAN.

ABSTRACT: In this work, we propose a novel GAN-based architecture, termed Physics-Inspired GAN (PI-GAN), to generate synthetic bimodal data comprising both intensity and phase images as produced through Fourier Ptychographic Microscopy (FPM). By explicitly incorporating the forward model of image formation into the GAN architecture, our approach ensures that the physical relationship between the intensity and phase modalities is preserved throughout the training and generation processes, therefore solving the mode collapse problem encountered in classical GANs. Our approach is evaluated for the classification of the five major types of white blood cells (WBCs) in peripheral blood smears, a domain where severe class imbalance is a major challenge. In particular, basophils represent less than 1% of ci

SUBMITTER: Hassini H 

PROVIDER: S-EPMC12660895 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature