Academic Journal
Προβολή σε EDS

Optical-Resolution Photoacoustic Microscopy-Based Virtual Staining: A Wavelet-Enhanced Contrastive Translation Approach With Structure Preservation.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Optical-Resolution Photoacoustic Microscopy-Based Virtual Staining: A Wavelet-Enhanced Contrastive Translation Approach With Structure Preservation.
Συγγραφείς: Mu R; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China., Guo S; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China., Xie Z; Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China., Li D; Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China., An X; Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China., Li J; China-Japan Friendship Hospital, Beijing, China., Han Y; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China., Liu Z; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China., Niu C; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China., Yang Q; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China., Liu Q; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.
Πηγή: Journal of biophotonics [J Biophotonics] 2026 Jun; Vol. 19 (6), pp. e70308.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print Cited Medium: Internet ISSN: 1864-0648 (Electronic) Linking ISSN: 1864063X NLM ISO Abbreviation: J Biophotonics Subsets: MEDLINE
Imprint Name(s): Original Publication: Weinheim : Wiley-VCH
Ιατρικοί όροι (MeSH): Image Processing, Computer-Assisted*/methods , Microscopy*/methods , Photoacoustic Techniques* , Signal-To-Noise Ratio* , Wavelet Analysis* , Optical Phenomena*, Colorectal Neoplasms/diagnostic imaging ; Colorectal Neoplasms/pathology ; Humans ; Staining and Labeling
Περίληψη: Photoacoustic microscopy (PAM) provides label-free and high-resolution imaging capabilities. However, its optical absorption-based contrast differs fundamentally from hematoxylin and eosin (H&E) staining, hindering integration into standard pathological interpretation workflows and limiting clinical translation and adoption. To address this limitation, we employ a low-cost, radiation-free 532 nm optical-resolution PAM (OR-PAM) system to construct a specimen-level cross-modal dataset for colorectal cancer. Based on this dataset, we propose a virtual H&E staining generation workflow that eliminates pixel-level alignment, enabling conversion of single-wavelength OR-PAM image data into H&E-style images with interpretable tissue structures. Quantitative evaluations demonstrate that our method's virtual sections outperform state-of-the-art unsupervised image-to-image translation models in overall structural fidelity, visual authenticity, and distribution consistency across key assessment metrics. This indicates that virtual staining can act as an efficient visualization layer for preliminary pathological assessment, offering a potential translational pathway for PAM in colorectal cancer clinical imaging.
(© 2026 Wiley‐VCH GmbH.)
References: H. Sung, J. Ferlay, R. L. Siegel, et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 71, no. 3 (2021): 209–249.
L. Pantanowitz, A. Sharma, A. B. Carter, T. Kurc, A. Sussman, and J. Saltz, “Twenty Years of Digital Pathology: An Overview of the Road Travelled and the Road Ahead,” Studies in Health Technology and Informatics 250 (2018): 53–61.
L. V. Wang and S. Hu, “Photoacoustic Tomography: In Vivo Imaging From Organelles to Organs,” Science 335, no. 6075 (2012): 1458–1462.
C. Li, L. Wang, and J. Xia, “Label‐Free Photoacoustic Microscopy of Peripheral Nerves,” Neurophotonics 7, no. 1 (2020): 015007.
A. James and M. K. Kalra, “The Translation of Medical Imaging AI Technologies Into Clinical Practice: Overcoming the Final Hurdles,” British Journal of Radiology 91, no. 1091 (2018): 20180429.
M. Fleming, S. Ravula, S. F. Tatishchev, and H. L. Wang, “Colorectal Carcinoma: Pathologic Aspects,” Journal of Gastrointestinal Oncology 3, no. 3 (2012): 153–173.
R. Awan, K. Sirinukunwattana, D. Epstein, et al., “Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images,” Scientific Reports 7 (2017): 16852.
Y. Rivenson, H. Wang, Z. Wei, et al., “PhaseStain: The Digital Staining of Label‐Free Quantitative Phase Microscopy Images Using Deep Learning,” Light: Science & Applications 8, no. 1 (2019): 2.
I. Goodfellow, J. Pouget‐Abadie, M. Mirza, et al., “Generative Adversarial Nets,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 27 (Curran Associates, Inc., 2014), 2672–2680.
Y. Zhao, R. Wu, and H. Dong, “Unpaired Image‐To‐Image Translation Using Adversarial Consistency Loss,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX, vol. 16 (Springer, 2020), 800–815.
H. Tang, H. Liu, D. Xu, H. S. Philip, and N. S. Torr, “AttentionGAN: Un‐Paired Image‐To‐Image Translation Using Attention‐Guided Generative Adversarial Networks,” IEEE Transactions on Neural Networks and Learning Systems 34, no. 4 (2023): 1972–1987.
P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image‐To‐Image Translation With Conditional Adversarial Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), 5967–5976.
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired Image‐To‐Image Translation Using Cycle‐Consistent Adversarial Networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) (IEEE, 2017), 2223–2232.
T. Park, A. A. Efros, R. Zhang, and J. Y. Zhu, “Contrastive Learning for Unpaired Image‐To‐Image Translation,” in Proceedings of the European Conference on Computer Vision (ECCV) (Springer, 2020), 319–335.
C. Yoon, E. Park, S. Misra, et al., “Deep Learning‐Based Virtual Staining, Segmentation, and Classification in Label‐Free Photoacoustic Histology of Human Specimens,” Light: Science & Applications 13 (2024): 226.
M. Boktor, J. E. D. Tweel, B. R. Ecclestone, J. A. Ye, P. Fieguth, and P. Haji Reza, “Multi‐Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing Images,” Scientific Reports 14, no. 1 (2024): 2009.
M. T. Martell, N. J. M. Haven, B. D. Cikaluk, et al., “Deep Learning‐Enabled Realistic Virtual Histology With Ultraviolet Photoacoustic Remote Sensing Microscopy,” Nature Communications 14, no. 1 (2023): 5967.
N. J. M. Haven, M. T. Martell, B. D. Cikaluk, et al., “Virtual Histopathology With Ultraviolet Scattering and Photoacoustic Remote Sensing Microscopy,” Optics Letters 46, no. 20 (2021): 5153–5156.
X. P. Wang, M. H. Cai, R. T. Mu, et al., “Photoacoustic Microscopy Imaging and Region‐Specific Segmentation of Ileocecal Malignant Tumor and Mixed Hemorrhoid Tissue Sections,” Journal of Biophotonics 18, no. 10 (2025): e202500169.
H. Zhang, Y. J. Yang, and W. Zeng, “Towards Semantically Continuous Unpaired Image‐To‐Image Translation via Margin Adaptive Contrastive Learning and Wavelet Transform,” IEEE Transactions on Pattern Analysis and Machine Intelligence 45, no. 12 (2023): 14321–14335.
P. Isola, J.‐Y. Zhu, T. Zhou, and A. A. Efros, “Image‐To‐Image Translation With Conditional Adversarial Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), 5967–5976.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs Trained by a Two Time‐Scale Update Rule Converge to a Local Nash Equilibrium,” in Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS, 2017).
M. Ski, D. J. Sutherland, M. Arbel, and A. Gretton, “Demystifying MMD GANs,” in Proceedings of the International Conference on Learning Representations (ICLR) (2018).
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing 13, no. 4 (2004): 600–612.
D. Mittal and R. Bhatia, “Image Quality Assessment Through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Journal of Image and Graphics 7, no. 2 (2019): 40–46.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2018).
Y. Xu, S. Xie, W. Wu, K. Zhang, M. Gong, and K. Batmanghelich, “Maximum Spatial Perturbation Consistency for Unpaired Image‐To‐Image Translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2022), 18311–18320.
W. Wang, W. Zhou, J. Bao, D. Chen, and H. Li, “Instance‐Wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image‐To‐Image Translation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (IEEE, 2021), 14020–14029.
M. Kassab, M. Jehanzaib, K. Başak, D. Demir, G. E. Keles, and M. Turan, “FFPE++: Improving the Quality of Formalin‐Fixed Paraffin‐Embedded Tissue Imaging via Contrastive Unpaired Image‐To‐Image Translation,” Medical Image Analysis 91 (2024): 102992.
Grant Information: 22019821001 Beijing Municipal Education Commission; L252031 Beijing Natural Science Foundation; 4244104 Beijing Natural Science Foundation; BIPTAAI-2021-004 Climbing Program Foundation at Beijing Institute of Petrochemical Technology
Contributed Indexing: Keywords: colorectal cancer; contrastive learning; deep learning; photoacoustic imaging; unpaired image translation
Entry Date(s): Date Created: 20260610 Date Completed: 20260610 Latest Revision: 20260610
Update Code: 20260611
DOI: 10.1002/jbio.70308
PMID: 42265985
Βάση Δεδομένων: MEDLINE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:cmedm&genre=article&issn=18640648&ISBN=&volume=19&issue=6&date=20260601&spage=e70308&pages=e70308&title=Journal of biophotonics&atitle=Optical-Resolution%20Photoacoustic%20Microscopy-Based%20Virtual%20Staining%3A%20A%20Wavelet-Enhanced%20Contrastive%20Translation%20Approach%20With%20Structure%20Preservation.&aulast=Mu%20R&id=DOI:10.1002/jbio.70308
    Name: Full Text Finder (for New FTF UI) (ns324271)
    Category: fullText
    Text: Full Text Finder
    MouseOverText: Full Text Finder
Header DbId: cmedm
DbLabel: MEDLINE
An: 42265985
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Optical-Resolution Photoacoustic Microscopy-Based Virtual Staining: A Wavelet-Enhanced Contrastive Translation Approach With Structure Preservation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Mu+R%22">Mu R</searchLink>; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Guo+S%22">Guo S</searchLink>; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Xie+Z%22">Xie Z</searchLink>; Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Li+D%22">Li D</searchLink>; Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.<br /><searchLink fieldCode="AU" term="%22An+X%22">An X</searchLink>; Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Li+J%22">Li J</searchLink>; China-Japan Friendship Hospital, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Han+Y%22">Han Y</searchLink>; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Liu+Z%22">Liu Z</searchLink>; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Niu+C%22">Niu C</searchLink>; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Yang+Q%22">Yang Q</searchLink>; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Liu+Q%22">Liu Q</searchLink>; Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22101318567%22">Journal of biophotonics</searchLink> [J Biophotonics] 2026 Jun; Vol. 19 (6), pp. e70308.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley-VCH%22">Wiley-VCH </searchLink><i>Country of Publication: </i>Germany <i>NLM ID: </i>101318567 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1864-0648 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%221864063X%22">1864063X </searchLink><i>NLM ISO Abbreviation: </i>J Biophotonics <i>Subsets: </i>MEDLINE
– Name: PublisherInfo
  Label: Imprint Name(s)
  Group: PubInfo
  Data: <i>Original Publication</i>: Weinheim : Wiley-VCH
– Name: SubjectMESH
  Label: MeSH Terms
  Group: Su
  Data: <searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted%22">Image Processing, Computer-Assisted*</searchLink>/<searchLink fieldCode="MM" term="%22Image+Processing%2C+Computer-Assisted+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Microscopy%22">Microscopy*</searchLink>/<searchLink fieldCode="MM" term="%22Microscopy+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Photoacoustic+Techniques%22">Photoacoustic Techniques*</searchLink> <br /><searchLink fieldCode="MM" term="%22Signal-To-Noise+Ratio%22">Signal-To-Noise Ratio*</searchLink> <br /><searchLink fieldCode="MM" term="%22Wavelet+Analysis%22">Wavelet Analysis*</searchLink> <br /><searchLink fieldCode="MM" term="%22Optical+Phenomena%22">Optical Phenomena*</searchLink><br /><searchLink fieldCode="MH" term="%22Colorectal+Neoplasms%22">Colorectal Neoplasms</searchLink>/<searchLink fieldCode="MH" term="%22Colorectal+Neoplasms+diagnostic+imaging%22">diagnostic imaging</searchLink> ; <searchLink fieldCode="MH" term="%22Colorectal+Neoplasms%22">Colorectal Neoplasms</searchLink>/<searchLink fieldCode="MH" term="%22Colorectal+Neoplasms+pathology%22">pathology</searchLink> ; <searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Staining+and+Labeling%22">Staining and Labeling</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Photoacoustic microscopy (PAM) provides label-free and high-resolution imaging capabilities. However, its optical absorption-based contrast differs fundamentally from hematoxylin and eosin (H&E) staining, hindering integration into standard pathological interpretation workflows and limiting clinical translation and adoption. To address this limitation, we employ a low-cost, radiation-free 532 nm optical-resolution PAM (OR-PAM) system to construct a specimen-level cross-modal dataset for colorectal cancer. Based on this dataset, we propose a virtual H&E staining generation workflow that eliminates pixel-level alignment, enabling conversion of single-wavelength OR-PAM image data into H&E-style images with interpretable tissue structures. Quantitative evaluations demonstrate that our method's virtual sections outperform state-of-the-art unsupervised image-to-image translation models in overall structural fidelity, visual authenticity, and distribution consistency across key assessment metrics. This indicates that virtual staining can act as an efficient visualization layer for preliminary pathological assessment, offering a potential translational pathway for PAM in colorectal cancer clinical imaging.<br /> (© 2026 Wiley‐VCH GmbH.)
– Name: Ref
  Label: References
  Group: RefInfo
  Data: H. Sung, J. Ferlay, R. L. Siegel, et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 71, no. 3 (2021): 209–249.<br />L. Pantanowitz, A. Sharma, A. B. Carter, T. Kurc, A. Sussman, and J. Saltz, “Twenty Years of Digital Pathology: An Overview of the Road Travelled and the Road Ahead,” Studies in Health Technology and Informatics 250 (2018): 53–61.<br />L. V. Wang and S. Hu, “Photoacoustic Tomography: In Vivo Imaging From Organelles to Organs,” Science 335, no. 6075 (2012): 1458–1462.<br />C. Li, L. Wang, and J. Xia, “Label‐Free Photoacoustic Microscopy of Peripheral Nerves,” Neurophotonics 7, no. 1 (2020): 015007.<br />A. James and M. K. Kalra, “The Translation of Medical Imaging AI Technologies Into Clinical Practice: Overcoming the Final Hurdles,” British Journal of Radiology 91, no. 1091 (2018): 20180429.<br />M. Fleming, S. Ravula, S. F. Tatishchev, and H. L. Wang, “Colorectal Carcinoma: Pathologic Aspects,” Journal of Gastrointestinal Oncology 3, no. 3 (2012): 153–173.<br />R. Awan, K. Sirinukunwattana, D. Epstein, et al., “Glandular Morphometrics for Objective Grading of Colorectal Adenocarcinoma Histology Images,” Scientific Reports 7 (2017): 16852.<br />Y. Rivenson, H. Wang, Z. Wei, et al., “PhaseStain: The Digital Staining of Label‐Free Quantitative Phase Microscopy Images Using Deep Learning,” Light: Science & Applications 8, no. 1 (2019): 2.<br />I. Goodfellow, J. Pouget‐Abadie, M. Mirza, et al., “Generative Adversarial Nets,” in Advances in Neural Information Processing Systems (NeurIPS), vol. 27 (Curran Associates, Inc., 2014), 2672–2680.<br />Y. Zhao, R. Wu, and H. Dong, “Unpaired Image‐To‐Image Translation Using Adversarial Consistency Loss,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX, vol. 16 (Springer, 2020), 800–815.<br />H. Tang, H. Liu, D. Xu, H. S. Philip, and N. S. Torr, “AttentionGAN: Un‐Paired Image‐To‐Image Translation Using Attention‐Guided Generative Adversarial Networks,” IEEE Transactions on Neural Networks and Learning Systems 34, no. 4 (2023): 1972–1987.<br />P. Isola, J. Y. Zhu, T. Zhou, and A. A. Efros, “Image‐To‐Image Translation With Conditional Adversarial Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), 5967–5976.<br />J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired Image‐To‐Image Translation Using Cycle‐Consistent Adversarial Networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) (IEEE, 2017), 2223–2232.<br />T. Park, A. A. Efros, R. Zhang, and J. Y. Zhu, “Contrastive Learning for Unpaired Image‐To‐Image Translation,” in Proceedings of the European Conference on Computer Vision (ECCV) (Springer, 2020), 319–335.<br />C. Yoon, E. Park, S. Misra, et al., “Deep Learning‐Based Virtual Staining, Segmentation, and Classification in Label‐Free Photoacoustic Histology of Human Specimens,” Light: Science & Applications 13 (2024): 226.<br />M. Boktor, J. E. D. Tweel, B. R. Ecclestone, J. A. Ye, P. Fieguth, and P. Haji Reza, “Multi‐Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing Images,” Scientific Reports 14, no. 1 (2024): 2009.<br />M. T. Martell, N. J. M. Haven, B. D. Cikaluk, et al., “Deep Learning‐Enabled Realistic Virtual Histology With Ultraviolet Photoacoustic Remote Sensing Microscopy,” Nature Communications 14, no. 1 (2023): 5967.<br />N. J. M. Haven, M. T. Martell, B. D. Cikaluk, et al., “Virtual Histopathology With Ultraviolet Scattering and Photoacoustic Remote Sensing Microscopy,” Optics Letters 46, no. 20 (2021): 5153–5156.<br />X. P. Wang, M. H. Cai, R. T. Mu, et al., “Photoacoustic Microscopy Imaging and Region‐Specific Segmentation of Ileocecal Malignant Tumor and Mixed Hemorrhoid Tissue Sections,” Journal of Biophotonics 18, no. 10 (2025): e202500169.<br />H. Zhang, Y. J. Yang, and W. Zeng, “Towards Semantically Continuous Unpaired Image‐To‐Image Translation via Margin Adaptive Contrastive Learning and Wavelet Transform,” IEEE Transactions on Pattern Analysis and Machine Intelligence 45, no. 12 (2023): 14321–14335.<br />P. Isola, J.‐Y. Zhu, T. Zhou, and A. A. Efros, “Image‐To‐Image Translation With Conditional Adversarial Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2017), 5967–5976.<br />M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs Trained by a Two Time‐Scale Update Rule Converge to a Local Nash Equilibrium,” in Advances in Neural Information Processing Systems (NeurIPS) (NeurIPS, 2017).<br />M. Ski, D. J. Sutherland, M. Arbel, and A. Gretton, “Demystifying MMD GANs,” in Proceedings of the International Conference on Learning Representations (ICLR) (2018).<br />Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” IEEE Transactions on Image Processing 13, no. 4 (2004): 600–612.<br />D. Mittal and R. Bhatia, “Image Quality Assessment Through FSIM, SSIM, MSE and PSNR—A Comparative Study,” Journal of Image and Graphics 7, no. 2 (2019): 40–46.<br />R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2018).<br />Y. Xu, S. Xie, W. Wu, K. Zhang, M. Gong, and K. Batmanghelich, “Maximum Spatial Perturbation Consistency for Unpaired Image‐To‐Image Translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (IEEE, 2022), 18311–18320.<br />W. Wang, W. Zhou, J. Bao, D. Chen, and H. Li, “Instance‐Wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image‐To‐Image Translation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (IEEE, 2021), 14020–14029.<br />M. Kassab, M. Jehanzaib, K. Başak, D. Demir, G. E. Keles, and M. Turan, “FFPE++: Improving the Quality of Formalin‐Fixed Paraffin‐Embedded Tissue Imaging via Contrastive Unpaired Image‐To‐Image Translation,” Medical Image Analysis 91 (2024): 102992.
– Name: GrantInfo
  Label: Grant Information
  Group: Grant
  Data: 22019821001 Beijing Municipal Education Commission; L252031 Beijing Natural Science Foundation; 4244104 Beijing Natural Science Foundation; BIPTAAI-2021-004 Climbing Program Foundation at Beijing Institute of Petrochemical Technology
– Name: SubjectMinor
  Label: Contributed Indexing
  Group:
  Data: <i>Keywords: </i>colorectal cancer; contrastive learning; deep learning; photoacoustic imaging; unpaired image translation
– Name: DateEntry
  Label: Entry Date(s)
  Group: Date
  Data: <i>Date Created: </i>20260610 <i>Date Completed: </i>20260610 <i>Latest Revision: </i>20260610
– Name: DateUpdate
  Label: Update Code
  Group: Date
  Data: 20260611
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1002/jbio.70308
– Name: AN
  Label: PMID
  Group: ID
  Data: 42265985
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=cmedm&AN=42265985
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/jbio.70308
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: e70308
    Subjects:
      – SubjectFull: Colorectal Neoplasms diagnostic imaging
        Type: general
      – SubjectFull: Colorectal Neoplasms pathology
        Type: general
      – SubjectFull: Humans
        Type: general
      – SubjectFull: Staining and Labeling
        Type: general
      – SubjectFull: Image Processing, Computer-Assisted methods
        Type: general
      – SubjectFull: Microscopy methods
        Type: general
      – SubjectFull: Photoacoustic Techniques
        Type: general
      – SubjectFull: Signal-To-Noise Ratio
        Type: general
      – SubjectFull: Wavelet Analysis
        Type: general
      – SubjectFull: Optical Phenomena
        Type: general
    Titles:
      – TitleFull: Optical-Resolution Photoacoustic Microscopy-Based Virtual Staining: A Wavelet-Enhanced Contrastive Translation Approach With Structure Preservation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Mu R
      – PersonEntity:
          Name:
            NameFull: Guo S
      – PersonEntity:
          Name:
            NameFull: Xie Z
      – PersonEntity:
          Name:
            NameFull: Li D
      – PersonEntity:
          Name:
            NameFull: An X
      – PersonEntity:
          Name:
            NameFull: Li J
      – PersonEntity:
          Name:
            NameFull: Han Y
      – PersonEntity:
          Name:
            NameFull: Liu Z
      – PersonEntity:
          Name:
            NameFull: Niu C
      – PersonEntity:
          Name:
            NameFull: Yang Q
      – PersonEntity:
          Name:
            NameFull: Liu Q
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: 2026 Jun
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 1864-0648
          Numbering:
            – Type: volume
              Value: 19
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Journal of biophotonics
              Type: main
ResultId 1