A systematic review of machine learning for digital stain processing in pathology

Abstract

Digital staining involves using methods such as Machine Learning (ML) to replace chemical staining in pathology. Staining adds contrast that makes cell details more visible under the microscope. However, chemical methods are slow, use toxic reagents, and require skilled personnel. In contrast, digital staining can generate images faster, reduce the need for reagents and specialized equipment, and minimize plastic and chemical waste, making the workflow more sustainable. This paper systematically reviews papers published on ML-based digital stain processing. We propose a new taxonomy that divides existing studies into five groups: stain normalization, stain augmentation, virtual staining, stain transformation, and hybrid approaches. In addition, we observed several trends from the reviewed papers. Finally, we outline open research directions.