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PathoEye: a novel deep learning framework for histopathological image analysis of skin tissue

Published: 16 December 2024 Publication History

Abstract

The whole-slide images (WSIs) examination of skin biopsy is the golden standard for pathological diagnosis of most skin diseases. However, the restricted computational resources limit the clinical applications of the large repositories of WSIs. Here, we presented a machine learning framework for WSI analysis in dermatology named PathoEye, which overcomes these limitations in WSI applications. We demonstrated that PathoEye can quantitatively measure the epidermis thickness, and the variance of rete ridge length decreases during skin aging. Then, the established classification models using PathoEye successfully discriminate the young and aged skin tissues. Interestingly, defects in dermis-epidermis junctional (DEJ) areas, also called basement membrane zone (BMZ), were found in the aged group compared with the young group. Further experimental analysis showed that the senescence cells were enriched in BMZ, and the turnover rate of the basement membrane (BM) components is delayed in aged skin, including COL17A1, COL4A2, and ITGA6. In summary, PathoEye successfully extracts the features of skin structure and components in WSIs. It also provides a comprehensive tool to characterize the unique features of different skin conditions associated with BMZ, ensuring its potential applications in skin disease diagnosis and treatment planning.

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PathoEye: a novel deep learning framework for histopathological image analysis of skin tissue

References

[1]
Famke Aeffner et al. 2019. Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association. Journal of Pathology Informatics 10, 1 (January 2019), 9.
[2]
Kaustav Bera et al. 2019. Artificial intelligence in digital pathology --- new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 16, 11 (November 2019), 703--715.
[3]
GTEx Consortium. 2013. The Genotype-Tissue Expression (GTEx) project. Nat Genet 45, 6 (June 2013), 580--585.
[4]
Tao Li et al. 2021. Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study. J Healthc Eng 2021, (2021), 5972962.

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  1. PathoEye: a novel deep learning framework for histopathological image analysis of skin tissue

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    cover image ACM Conferences
    BCB '24: Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
    November 2024
    614 pages
    ISBN:9798400713026
    DOI:10.1145/3698587
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Publication History

    Published: 16 December 2024

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    Author Tags

    1. Basement membrane
    2. Deep learning
    3. Skin
    4. Whole-slide images

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    Overall Acceptance Rate 254 of 885 submissions, 29%

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