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A high‐efficiency computer‐aided diagnostic model of ovarian cancer was developed, integrating SHG imaging technology for non‐invasive imaging of living tissue and machine learning method based on radiomics and TPOT. This model can rapidly, non‐destructively, and accurately perform ovarian cancer di...
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Veröffentlicht in: | Journal of biophotonics 2020-09, Vol.13 (9) |
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Hauptverfasser: | , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
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Zusammenfassung: | A high‐efficiency computer‐aided diagnostic model of ovarian cancer was developed, integrating SHG imaging technology for non‐invasive imaging of living tissue and machine learning method based on radiomics and TPOT. This model can rapidly, non‐destructively, and accurately perform ovarian cancer diagnosis and has great potential in improving diagnostic efficacy and efficiency of medical pathologists.
Further details can be found in the article by Guangxing Wang, Yang Sun, Youting Chen, Qiqi Gao, Dongqing Peng, Hongxin Lin, Zhenlin Zhan, Zhiyi Liu, and Shuangmu Zhuo (
e202000050
).
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ISSN: | 1864-063X 1864-0648 |
DOI: | 10.1002/jbio.202070022 |