Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Shu Wang; Junlin Pan; Xiao Zhang; Yueying Li; Wenxi Liu; Ruolan Lin; Xingfu Wang; Deyong Kang; Zhijun Li; Feng Huang; Liangyi Chen; Jianxin Chen

doi:10.1038/s41377-024-01597-w

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Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy

Reviews

- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy
- Light: Science & Applications Vol. 13, Issue 12, Pages: 2887-2911(2024)
- Affiliations：
  
  1.School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
  2.Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou 350007, China
  3.College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
  4.Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
  5.Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China
  6.Department of Pathology, Fujian Medical University Union Hospital, Fuzhou 350001, China
  7.New Cornerstone Laboratory, State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing 100091, China
- Author bio：
  
  Feng Huang (huangf@fzu.edu.cn)
  Liangyi Chen (lychen@pku.edu.cn)
  Jianxin Chen (chenjianxin@fjnu.edu.cn)
- Funds：
- DOI：10.1038/s41377-024-01597-w
  CLC：
- Published：31 December 2024，
  
  Published Online：14 September 2024，
  
  Received：26 February 2024，
  
  Revised：04 August 2024，
  
  Accepted：21 August 2024
- Accepted：
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Wang, S. et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy. Light: Science & Applications, 13, 2887-2911 (2024).
DOI：

Wang, S. et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy. Light: Science & Applications, 13, 2887-2911 (2024). DOI： 10.1038/s41377-024-01597-w.

摘要

Abstract

Diagnostic pathology

historically dependent on visual scrutiny by experts

is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements

the variability in pathologists’ subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies

there is an increasing demand for accurate pathological diagnoses. Consequently

traditional diagnostic pathology is evolving towards “next-generation diagnostic pathology”

prioritizing on the development of a multi-dimensional

intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues

multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis

holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review

we systematically outline the applications of MPM in pathological diagnosis across various human diseases

and summarize common multiphoton diagnostic features. Moreover

we examine the significant role of AI in enhancing multiphoton pathological diagnosis

including aspects such as image preprocessing

refined differential diagnosis

and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI

encompassing equipment

datasets

analytical models

and integration into the existing clinical pathways. Finally

the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks

aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.

关键词

Keywords

references

Liu, J. T. C. et al. Nondestructive 3D pathology with light-sheet fluorescence microscopy for translational research and clinical assays.Annu. Rev. Anal. Chem.16, 231–252 (2023)..

Ghaznavi, F. et al. Digital imaging in pathology: whole-slide imaging and beyond.Annu. Rev. Pathol. Mechanisms Dis.8, 331–359 (2013)..

Peng, L. T., Zhu, C. L.&Bian, L. H. U-shape transformer for underwater image enhancement.IEEE Trans. Image Process.32, 3066–3079 (2023)..

Wu, Z. et al. Three-dimensional nanoscale reduced-angle ptycho-tomographic imaging with deep learning (RAPID).eLight3, 7 (2023)..

Lin, H.&Cheng, J. X. Computational coherent Raman scattering imaging: breaking physical barriers by fusion of advanced instrumentation and data science.eLight3, 6 (2023)..

Zhang, Y. X. et al. Single-source domain expansion network for cross-scene hyperspectral image classification.IEEE Trans. Image Process.32, 1498–1512 (2023)..

Zou, Z. X. et al. Object detection in 20 years: a survey.Proc. IEEE111, 257–276 (2023)..

Lee, M. et al. Unsupervised video object segmentation via prototype memory network. In2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 5913–5923 (IEEE, 2023).

Gehrung, M. et al. Triage-driven diagnosis of Barrett’s esophagus for early detection of esophageal adenocarcinoma using deep learning.Nat. Med.27, 833–841 (2021)..

Amgad, M. et al. A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer.Nat. Med.30, 85–97 (2024)..

Lu, M. Y. et al. AI-based pathology predicts origins for cancers of unknown primary.Nature594, 106–110 (2021)..

Myung, K.&Kim Phase microscopy and surface profilometry by digital holography.Light.: Adv. Manuf.3, 19 (2022)..

Utadiya, Subhash et al. Integrated self-referencing single shot digital holographic microscope and optical tweezer.Light.: Adv. Manuf.3, 37 (2022)..

Gao, Yunhui&Cao, Liangcai Iterative projection meets sparsityregularization: towards practical single-shot quantitative phase imaging with in-line holography.Light.: Adv. Manuf.4, 6 (2023)..

Li, Yuhang et al. Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network.Light.: Adv. Manuf.4, 19 (2023)..

Zvagelsky, Roman et al. Towards in-situ diagnostics of multi-photon 3D laser printing using optical coherence tomography.Light.: Adv. Manuf.3, 39 (2022)..

Wang, S. et al. Resection-inspired histopathological diagnosis of cerebral cavernous malformations using quantitative multiphoton microscopy.Theranostics12, 6595–6610 (2022)..

Zhang, G. Y. et al. Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study.Lancet Digital Health3, e665–e675 (2021)..

Zhao, J. H. et al. PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI.Light Sci. Appl.12, 297 (2023)..

Cao, R. et al. Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy.Nat. Biomed. Eng.7, 124–134 (2023)..

Hollon, T. C. et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks.Nat. Med.26, 52–58 (2020)..

Chen, J. H. et al. Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer.BMC Cancer23, 530 (2023)..

Xi, G. Q. et al. Large-scale tumor-associated collagen signatures identify high-risk breast cancer patients.Theranostics11, 3229–3243 (2021)..

He, J. J. et al. Prognostic value of tumour-infiltrating lymphocytes based on the evaluation of frequency in patients with oestrogen receptor-positive breast cancer.Eur. J. Cancer154, 217–226 (2021)..

Provenzano, P. P. et al. Collagen reorganization at the tumor-stromal interface facilitates local invasion.BMC Med.4, 38 (2006)..

Chen, D. X. et al. Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram.Nat. Commun.12, 179 (2021)..

Chen, D. X. et al. Association of the collagen signature in the tumor microenvironment with lymph node metastasis in early gastric cancer.JAMA Surg.154, e185249 (2019)..

Wang, Q. Q. et al. Differentiating morphea from lichen sclerosus by using multiphoton microscopy combined with U-Net model for elastic fiber segmentation.J. Biophotonics16, e202300078 (2023)..

Cai, S. J. et al. Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network.Quant. Imaging Med. Surg.10, 1275–1285 (2020)..

Terradillos, E. et al. Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods.J. Pathol. Inform.12, 27 (2021)..

Wen, B. L. et al. Texture analysis applied to second harmonic generation image data for ovarian cancer classification.J. Biomed. Opt.19, 096007 (2014)..

Wen, B. et al. 3D texture analysis for classification of second harmonic generation images of human ovarian cancer.Sci. Rep.6, 35734 (2016)..

Kistenev, Y. V. et al. Application of multiphoton imaging and machine learning to lymphedema tissue analysis.Biomed. Opt. Express10, 3353–3368 (2019)..

Wang, G. X. et al. Automated ovarian cancer identification using end-to-end deep learning and second harmonic generation imaging.IEEE J. Sel. Top. Quantum Electron.29, 7200609 (2023)..

Lin, H. X. et al. Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning.J. Biophotonics12, e201800435 (2019)..

Yang, Q. Q. et al. Epithelium segmentation and automated gleason grading of prostate cancer via deep learning in label-free multiphoton microscopic images.J. Biophotonics13, e201900203 (2020)..

Huang, X. X. et al. Detection of fibrotic changes in the progression of liver diseases by label-free multiphoton imaging.J. Biophotonics16, e202300153 (2023)..

Xi, G. Q. et al. Automated classification of breast cancer histologic grade using multiphoton microscopy and generative adversarial networks.J. Phys. D: Appl. Phys.56, 015401 (2023)..

Meng, J. et al. Mapping variation of extracellular matrix in human keloid scar by label-free multiphoton imaging and machine learning.J. Biomed. Opt.28, 045001 (2023)..

Blokker, M. et al. Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning.Sci. Rep.12, 11334 (2022)..

You, S. X. et al. Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology.npj Precis. Oncol.3, 33 (2019)..

Qiu, L. D. et al. Intratumor graph neural network recovers hidden prognostic value of multi-biomarker spatial heterogeneity.Nat. Commun.13, 4250 (2022)..

Fang, N. et al. Automatic and label-free identification of blood vessels in gliomas using the combination of multiphoton microscopy and image analysis.J. Biophotonics12, e201900006 (2019)..

Mehidine, H. et al. Multimodal imaging to explore endogenous fluorescence of fresh and fixed human healthy and tumor brain tissues.J. Biophotonics12, e201800178 (2019)..

Fang, N. et al. Rapid, label-free detection of intracranialgerminoma using multiphoton microscopy.Neurophotonics6, 035014 (2019)..

Fang, N. et al. A pilot study of using multiphoton microscopy to diagnose schwannoma.J. Phys. D: Appl. Phys.52, 415401 (2019)..

Lin, P. H. et al. Diagnosing pituitary adenoma in unstained sections based on multiphoton microscopy.Pituitary21, 362–370 (2018)..

He, J. J. et al. Label-free detection of invasive micropapillary carcinoma of the breast using multiphoton microscopy.J. Biophotonics16, e202200224 (2023)..

Han, Z. H. et al. Detection of pathological response of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer using multiphoton microscopy.J. Biophotonics16, e202200274 (2023)..

Gavgiotaki, E. et al. Third Harmonic Generation microscopy distinguishes malignant cell grade in human breast tissue biopsies.Sci. Rep.10, 11055 (2020)..

Shen, T. F. et al. Monitoring the progression of lobular breast carcinoma using multiphoton microscopy.Laser Phys. Lett.16, 105601 (2019)..

Sun, Y. et al. Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging.Sci. Adv.4, eaau5603 (2018)..

Nie, Y. T. et al. Differentiating the two main histologic categories of fibroadenoma tissue from normal breast tissue by using multiphoton microscopy.J. Microsc.258, 79–85 (2015)..

Matsui, T. et al. Non-labeling multiphoton excitation microscopy as a novel diagnostic tool for discriminating normal tissue and colorectal cancer lesions.Sci. Rep.7, 6959 (2017)..

Li, L. H. et al. Visualization of tumor response to neoadjuvant therapy for rectal carcinoma by nonlinear optical imaging.IEEE J. Sel. Top. Quantum Electron.22, 158–163 (2016)..

Li, L. H. et al. Detection of morphologic alterations in rectal carcinoma following preoperative radiochemotherapy based on multiphoton microscopy imaging.BMC Cancer15, 142 (2015)..

Yan, J. et al. Real-time optical diagnosis for surgical margin in low rectal cancer using multiphoton microscopy.Surgical Endosc.28, 36–41 (2014)..

Methods in Brief. Label-free histopathology.Nat. Methods13, 815 (2016)..

Huang, S. H., Heikal, A. A.&Webb, W. W. Two-photon fluorescence spectroscopy and microscopy of NAD(P)H and flavoprotein.Biophysical J.82, 2811–2825 (2002)..

Chen, X. Y. et al. Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure.Nat. Protoc.7, 654–669 (2012)..

Van Steenbergen, V. et al. Molecular understanding of label-free second harmonic imaging of microtubules.Nat. Commun.10, 3530 (2019)..

Nucciotti, V. et al. Probing myosin structural conformation in vivo by second-harmonic generation microscopy.Proc. Natl Acad. Sci. USA107, 7763–7768 (2010)..

Zipfel, W. R. et al. Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation.Proc. Natl Acad. Sci. USA100, 7075–7080 (2003)..

Monici, M. Cell and tissue autofluorescence research and diagnostic applications.Biotechnol. Annu. Rev.11, 227–256 (2005)..

Denk, W., Strickler, J. H.&Webb, W. W. Two-photon laser scanning fluorescence microscopy.Science248, 73–76 (1990)..

Petersson, P. Two-Photon Excited Laser Scanning Confocal Microscopy.Lund Reports on Atomic PhysicsLRAP-226 (1997).

Zong, W. J. et al. Fast high-resolution miniature two-photon microscopy for brain imaging in freely behaving mice.Nat. Methods14, 713–719(2017)..

Wei, M. J. Space station two-photon microscope. Preprint athttps://news.cgtn.com/news/2023-04-14/China-Space-Station-achieves-100-regeneration-of-oxygen-resources-1iZpNmOwR4k/index.htmlhttps://news.cgtn.com/news/2023-04-14/China-Space-Station-achieves-100-regeneration-of-oxygen-resources-1iZpNmOwR4k/index.html(2023).

Masters, B. R., So, P. T.&Gratton, E. Multiphoton excitation fluorescence microscopy and spectroscopy of in vivo human skin.Biophysical J.72, 2405–2412 (1997)..

Cox, G. et al. 3-Dimensional imaging of collagen using second harmonic generation.J. Struct. Biol.141, 53–62 (2003)..

Tu, H. H. et al. Concurrence of extracellular vesicle enrichment and metabolic switch visualized label-free in the tumor microenvironment.Sci. Adv.3, e1600675 (2017)..

Borhani, N. et al. Digital staining through the application of deep neural networks to multi-modal multi-photon microscopy.Biomed. Opt. Express10, 1339–1350 (2019)..

Shen, B. L. et al. Deep learning autofluorescence-harmonic microscopy.Light Sci. Appl.11, 76 (2022)..

Göppert-Mayer, M. Über elementarakte mit zwei quantensprüngen.Ann. der Phys.401, 273–294 (1931)..

Kaiser, W.&Garrett, C. G. B. Two-photon excitation in CaF2: Eu2+.Phys. Rev. Lett.7, 229–231 (1961)..

Hellwarth, R.&Christensen, P. Nonlinear optical microscopic examination of structure in polycrystalline ZnSe.Opt. Commun.12, 318–322 (1974)..

Huff, J. The Fast mode for Zeiss LSM 880 with Airyscan: high-speed confocal imaging with super-resolution and improved signal-to-noise ratio.Nature Methods13, ⅰ–ⅱ (2016)..

Chang, L. Nikon’s large-format multiphoton system for intravital imaging.Nat. Methods12, ⅲ–ⅳ (2015)..

Yan, J. et al. Preclinical study of using multiphoton microscopy to diagnose liver cancer and differentiate benign and malignant liver lesions.J. Biomed. Opt.17, 026004 (2012)..

Wu, Y. et al. Identifying three different architectural subtypes of mammary ductal carcinoma in situ using multiphoton microscopy.J. Phys. D: Appl. Phys.48, 405401 (2015)..

Fang, N. et al. Quantitative assessment of microenvironment characteristics and metabolic activity in glioma via multiphoton microscopy.J. Biophotonics12, e201900136 (2019)..

Han, X. H. et al. Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: an innovative method with H&E-stained and multiphoton microscopy images.Int. J. Cancer154, 1802–1813 (2024)..

Zhang, H. et al. Optical biopsy of laryngeal lesions using femtosecond multiphoton microscopy.Biomed. Opt. Express12, 1308–1319 (2021)..

Zeng, Y. P. et al. Intraoperative assisting diagnosis of esophageal submucosal cancer using multiphoton microscopy.Laser Phys. Lett.15, 075603 (2018)..

Wang, S. et al. Label-free detection of the architectural feature of blood vessels in glioblastoma based on multiphoton microscopy.IEEE J. Sel. Top. Quantum Electron.27, 7200907 (2021)..

Fang, N. et al. Label-free detection of brain invasion in meningiomas by multiphoton microscopy.Laser Phys. Lett.16, 015603 (2019)..

Jain, M. et al. Multiphoton microscopy: a potential “optical biopsy” tool for real-time evaluation of lung tumors without the need for exogenous contrast agents.Arch. Pathol. Lab. Med.138, 1037–1047 (2014)..

Jain, M. et al. Multiphoton microscopy: a potential intraoperative tool for the detection of carcinoma in situ in human bladder.Arch. Pathol. Lab. Med.139, 796–804 (2015)..

Yan, J. et al. Real-time optical diagnosis of gastric cancer with serosal invasion using multiphoton imaging.Sci. Rep.6, 31004 (2016)..

Jain, M. et al. Exploring multiphoton microscopy as a novel tool to differentiate chromophobe renal cell carcinoma from oncocytoma in fixed tissue sections.Arch. Pathol. Lab. Med.142, 383–390 (2018)..

Xu, J. et al. Multiphoton microscopy for label-free identification of intramural metastasis in human esophageal squamous cell carcinoma.Biomed. Opt. Express8, 3360–3368 (2017)..

Xu, J. et al. Identifying the neck margin status of ductal adenocarcinoma in the pancreatic head by multiphoton microscopy.Sci. Rep.7, 4586 (2017)..

Ling, Y. T. et al. Second harmonic generation (SHG) imaging of cancer heterogeneity in ultrasound guided biopsies of prostate in men suspected with prostate cancer.J. Biophotonics10, 911–918 (2017)..

Pouli, D. et al. Two-photon images reveal unique texture features for label-free identification of ovarian cancer peritoneal metastases.Biomed. Opt. Express10, 4479–4488 (2019)..

Zhan, H. L. et al. Identification of the tumor boundary of hilar cholangiocarcinoma based on multiphoton microscopy.IEEE Photonics J.14, 4050107 (2022)..

Pukaluk, A. et al. Changes in the microstructure of the human aortic medial layer under biaxial loading investigated by multi-photon microscopy.Acta Biomaterialia151, 396–413 (2022)..

Yang, Y. L. et al. Multiphoton microscopy providing pathological-level quantification of myocardial fibrosis in transplanted human heart.Lasers Med. Sci.37, 2889–2898 (2022)..

Xi, G. Q. et al. Label-free imaging of blood vessels in human normal breast and breast tumor tissue using multiphoton microscopy.Scanning2019, 5192875 (2019)..

Liu, Y. L. et al. Quantitative analysis of collagen morphology in breast cancer from millimeter scale using multiphoton microscopy.J. Innovative Optical Health Sci.16, 2243003 (2023)..

Xi, G. Q. et al. Rapid label-free detection of early-stage lung adenocarcinoma and tumor boundary via multiphoton microscopy.J. Biophotonics16, e202300172 (2023)..

Golaraei, A. et al. Polarimetric second-harmonic generation microscopy of the hierarchical structure of collagen in stage Ⅰ-Ⅲ non-small cell lung carcinoma.Biomed. Opt. Express11, 1851–1863 (2020)..

Li, L. H. et al. Label-free identification of early gastrointestinal neuroendocrine tumors via biomedical multiphoton microscopy and automatic image analysis.IEEE Access8, 105681–105689 (2020)..

Zheng, X. L. et al. Margin diagnosis for endoscopic submucosal dissection of early gastric cancer using multiphoton microscopy.Surgical Endosc.34, 408–416 (2020)..

Lin, H. X. et al. Label-free classification of hepatocellular-carcinoma grading using second harmonic generation microscopy.Biomed. Opt. Express9, 3783–3793 (2018)..

Chen, J. et al. Optical characterization of lesions and identification of surgical margins in pancreatic metastasis from renal cell carcinoma by using two-photon excited fluorescence microscopy.Laser Phys.24, 115603 (2014)..

Chen, Y. T. et al. Multiphoton microscopy as a diagnostic imaging modality for pancreatic neoplasms without hematoxylin and eosin stains.J. Biomed. Opt.19, 96008 (2014)..

Qian, S. H. et al. Identification of human ovarian cancer relying on collagen fiber coverage features by quantitative second harmonic generation imaging.Opt. Express30, 25718–25733 (2022)..

Pouli, D. et al. Label-free, high-resolution optical metabolic imaging of human cervical precancers reveals potential for intraepithelial neoplasia diagnosis.Cell Rep. Med.1, 100017 (2020)..

Pradère, B. et al. Two-photon optical imaging, spectral and fluorescence lifetime analysis to discriminate urothelial carcinoma grades.J. Biophotonics11, e201800065 (2018)..

Jain, M. et al. Multiphoton microscopy in the evaluation of human bladder biopsies.Arch. Pathol. Lab. Med.136, 517–526 (2012)..

Huland, D. M. et al. Multiphoton gradient index endoscopy for evaluation of diseased human prostatic tissue ex vivo.J. Biomed. Opt.19, 116011 (2014)..

Huttunen, M. J. et al. Multiphoton microscopy of the dermoepidermal junction and automated identification of dysplastic tissues with deep learning.Biomed. Opt. Express11, 186–199 (2020)..

Arginelli, F. et al. High resolution diagnosis of common nevi by multiphoton laser tomography and fluorescence lifetime imaging.Ski. Res. Technol.19, 194–204 (2013)..

Seidenari, S. et al. Diagnosis of BCC by multiphoton laser tomography.Ski. Res. Technol.19, e297–e304 (2013)..

Wang, S. et al. Optical visualization of cerebral cortex by label-free multiphoton microscopy.IEEE J. Sel. Top. Quantum Electron.25, 6800508 (2019)..

Batista, A. et al. High-resolution, label-free two-photon imaging of diseased human corneas.J. Biomed. Opt.23, 036002 (2018)..

Jain, M. et al. A component-by-component characterisation of high-risk atherosclerotic plaques by multiphoton microscopic imaging.J. Microsc.268, 39–44 (2017)..

Wong, S. et al. Evaluation of barrett esophagus by multiphoton microscopy.Arch. Pathol. Lab. Med.138, 204–212 (2014)..

Kottmann,R. M. et al. Second harmonic generation microscopy reveals altered collagen microstructure in usual interstitial pneumonia versus healthy lung.Respiratory Res.16, 61 (2015)..

Tilbury, K. et al. Second harmonic generation microscopy analysis of extracellular matrix changes in human idiopathic pulmonary fibrosis.J. Biomed. Opt.19, 086014 (2014)..

Tjin, G. et al. Quantification of collagen I in airway tissues using second harmonic generation.J. Biomed. Opt.19, 036005 (2014)..

Zhang, R. L. et al. Label-free identification of human coronary atherosclerotic plaque based on a three-dimensional quantitative assessment of multiphoton microscopy images.Biomed. Opt. Express12, 2979–2995 (2021)..

Goh, G. B. B. et al. Quantification of hepatic steatosis in chronic liver disease using novel automated method of second harmonic generation and two-photon excited fluorescence.Sci. Rep.9, 2975 (2019)..

Jiang, S. Y. et al. Mapping the 3D remodeling of the extracellular matrix in human hypertrophic scar by multi-parametric multiphoton imaging using endogenous contrast.Heliyon9, e13653 (2023)..

Han, Y. et al. Non-invasive imaging of pathological scars using a portable handheld two-photon microscope.Chin. Med. J.137, 329–337 (2024)..

Utino, F. L. et al. Second-harmonic generation imaging analysis can help distinguish sarcoidosis from tuberculoid leprosy.J. Biomed. Opt.23, 126001 (2018)..

Springer, S. et al. Examination of wound healing after curettage by multiphoton tomography of human skin in vivo.Ski. Res. Technol.23, 452–458 (2017)..

Huck, V. et al. From morphology to biochemical state - intravital multiphoton fluorescence lifetime imaging of inflamed human skin.Sci. Rep.6, 22789 (2016)..

Koehler, M. J. et al. Acute UVB-induced epidermal changes assessed by multiphoton laser tomography.Ski. Res. Technol.21, 137–143 (2015)..

Tong, P. L. et al. A quantitative approach to histopathological dissection of elastin-related disorders using multiphoton microscopy.Br. J. Dermatol.169, 869–879 (2013)..

Lin, L. H. et al. Visualization of dermal alteration in skin lesions with discoid lupus erythematosus by multiphoton microscopy.Laser Phys.23, 045606 (2013)..

Liu, Z. Y. et al. 3D organizational mapping of collagen fibers elucidates matrix remodeling in a hormone-sensitive 3D breast tissue model.Biomaterials179, 96–108 (2018)..

Zhou, Z. et al. Adaptive image enhancement for tracing 3D morphologies of neurons and brain vasculatures.Neuroinformatics13, 153–166 (2015)..

Ye, C. T. et al. Learned, uncertainty-driven adaptive acquisition for photon-efficient multiphoton microscopy. Preprint athttps://doi.org/10.48550/arXiv.2310.16102https://doi.org/10.48550/arXiv.2310.16102(2023).

Wang, S. et al. A deep learning-based stripe self-correction method for stitched microscopic images.Nat. Commun.14, 5393 (2023)..

McAleer, S. et al. Deep learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging in tissues with high and low light sensitivity.Transl. Vis. Sci. Technol.10, 30 (2021)..

Lin, G. M. et al. Enhanced resnet-based super-resolution method for two-photon microscopy image.Signal, Image Video Process.16, 2157–2163 (2022)..

Zhao, Z. W. et al. Deep learning-based high-speed, large-field, and high-resolution multiphoton imaging.Biomed. Opt. Express14, 65–80 (2023)..

Picon, A. et al. Novel pixelwise co-registered hematoxylin-eosin and multiphoton microscopy image dataset for human colon lesion diagnosis.J. Pathol. Inform.13, 100012 (2022)..

Shi, J. D. et al. Weakly supervised identification of microscopic human breast cancer-related optical signaturesfrom normal-appearing breast tissue.Biomed. Opt. Express14, 1339–1354 (2023)..

Keikhosravi, A. et al. Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis.Commun. Biol.3, 414 (2020)..

Sugita, Y. et al. Intraoperative rapid diagnosis of primary central nervous system lymphomas: advantages and pitfalls.Neuropathology34, 438–445 (2014)..

Tu, H. H. et al. Stain-free histopathology by programmable supercontinuum pulses.Nat. Photonics10, 534–540 (2016)..

Li, Z. Y. et al. Toward source-free cross tissues histopathological cell segmentation via target-specific finetuning.IEEE Trans. Med. Imaging42, 2666–2677 (2023)..

Shen, N. et al. SCANet: a unified semi-supervised learning framework for vessel segmentation.IEEE Trans. Med. Imaging42, 2476–2489 (2023)..

Li, H. L. et al. Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7454–7463 (IEEE, 2023).

Chen, Y. C.&Lu, C. S. RankMix: data augmentation for weakly supervised learning of classifying whole slide images with diverse sizes and imbalanced categories. InIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 23936–23945 (IEEE, 2023).

Wagner, S. J. et al. Transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study.Cancer Cell41, 1650–1661.e4 (2023)..

Chen, R. J. et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning.Cancer Cell40, 865–878.e6 (2022)..

Zhang, D. W. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology.Nat. Biotechnol.https://doi.org/10.1038/s41587-023-02019-9 (2024).

Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.Nat. Cancer1, 800–810 (2020)..

Lu, M. Y. et al. A multimodal generative AI Copilot for human pathology.Naturehttps://doi.org/10.1038/s41586-024-07618-3 (2024).

Ghahremani, P. et al. Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification.Nat. Mach. Intell.4, 401–412 (2022)..

Pohlkamp, C. et al. Machine learning (ML) can successfully support microscopic differential counts of peripheral blood smears in a high throughput hematology laboratory.Blood136, 45–46 (2020)..

Nasrallah, M. P. et al. Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma.Med4, 526–540.e4 (2023)..

Gómez-de-Mariscal, E. et al. DeepImageJ: a user-friendly environment to run deep learning models in ImageJ.Nat. Methods18, 1192–1195 (2021)..

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