https://scholars.lib.ntu.edu.tw/handle/123456789/627338
Title: | End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based model | Authors: | Liu, Zhi-Yong Lin, Chi-Hung Wang, Hsiang-Sheng Wen, Mei-Chin WEI-CHOU LIN Huang, Shun-Chen Tu, Kun-Hua Kuo, Chang-Fu Chen, Tai-Di |
Keywords: | interstitial fibrosis; machine learning; reliability; reproducibility; whole-slide imaging | Issue Date: | 19-Oct-2022 | Publisher: | OXFORD UNIV PRESS | Journal Volume: | 37 | Journal Issue: | 11 | Start page/Pages: | 2093 | Source: | Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association | Abstract: | The extent of interstitial fibrosis in the kidney not only correlates with renal function at the time of biopsy but also predicts future renal outcome. However, its assessment by pathologists lacks good agreement. The aim of this study is to construct a machine learning-based model that enables automatic and reliable assessment of interstitial fibrosis in human kidney biopsies. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/627338 | ISSN: | 0931-0509 | DOI: | 10.1093/ndt/gfac143 |
Appears in Collections: | 病理學科所 |
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