Noninvasive Nonlinear Optical Computational Histology

Abstract

Cancer remains a global health challenge, demanding early detection and accurate diagnosis for improved patient outcomes. An intelligent paradigm is introduced that elevates label‐free nonlinear optical imaging with contrastive patch‐wise learning, yielding stain‐free nonlinear optical computational histology (NOCH). NOCH enables swift, precise diagnostic analysis of fresh tissues, reducing patient anxiety and healthcare costs. Nonlinear modalities are evaluated, including stimulated Raman scattering and multiphoton imaging, for their ability to enhance tumor microenvironment sensitivity, pathological analysis, and cancer examination. Quantitative analysis confirmed that NOCH images accurately reproduce nuclear morphometric features across different cancer stages. Key diagnostic features, such as nuclear morphology, size, and nuclear‐cytoplasmic contrast, are well preserved. NOCH models also demonstrate promising generalization when applied to other pathological tissues. The study unites label‐free nonlinear optical imaging with histopathology using contrastive learning to establish stain‐free computational histology. NOCH provides a rapid, non‐invasive, and precise approach to surgical pathology, holding immense potential for revolutionizing cancer diagnosis and surgical interventions.

Stain-Free Histopathology with Stimulated Raman Scattering Microscopy

“…Falahkheirkhah et al utilized GAN formation and CycleGAN to generate virtual H&E images of prostate tissues interpretable to pathologists. Shen et al . introduced an intelligent paradigm that elevates label-free nonlinear optical computational histology (NOCH) using contrastive deep learning.…”

“…Falahkheirkhah et al 139 utilized GAN formation and CycleGAN to generate virtual H&E images of prostate tissues interpretable to pathologists. Shen et al 140 introduced an intelligent paradigm that elevates label-free nonlinear optical computational histology (NOCH) using contrastive deep learning. The above deep-learning-based virtual staining methods have shown great potential of stain- free histopathology to yield FFPE-grade results within a few minutes.…”