Noninvasive Nonlinear Optical Computational Histology

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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.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
Noninvasive Nonlinear Optical Computational Histology ; day:14 ; month:12 ; year:2023 ; extent:14
Advanced science ; (14.12.2023) (gesamt 14)

Urheber
Shen, Binglin
Li, Zhenglin
Pan, Ying
Guo, Yuan
Yin, Zongyi
Hu, Rui
Qu, Junle
Liu, Liwei

DOI
10.1002/advs.202308630
URN
urn:nbn:de:101:1-2023121514224511818702
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
15.08.2025, 07:33 MESZ

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Beteiligte

  • Shen, Binglin
  • Li, Zhenglin
  • Pan, Ying
  • Guo, Yuan
  • Yin, Zongyi
  • Hu, Rui
  • Qu, Junle
  • Liu, Liwei

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