[1] |
郑闪,孙丰龙,张慧娟,等. 人工智能在肿瘤组织病理学的研究现状[J]. 中华肿瘤杂志,2018, 40(12): 885-889.
|
[2] |
张世豪,冼丽英,高敏,等. 基于深度学习的人工智能在病理诊断的应用进展与展望[J]. 中国医学创新,2018, 15(25): 130-133.
|
[3] |
Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks [J]. Science, 2006, 313(5786): 504-507.
|
[4] |
Yu KH, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features [J]. Nat Commun, 2016, 7: 12474.
|
[5] |
邓杨,包骥. 数字病理中计算机辅助诊断研究展望[J]. 实用医院临床杂志,2017, 14(5): 10-12.
|
[6] |
张楠,鲁海珍,应建明,等. 人工智能在诊断病理中的应用进展[J]. 诊断病理学杂志,2019, 26(3): 183-185.
|
[7] |
许燕,汤烨,闫雯,等. 病理人工智能的现状和展望[J]. 中华病理学杂志,2017, 46(9): 593-595.
|
[8] |
Barisoni L, Nast CC, Jennette JC, et al. Digital pathology evaluation in the multicenter Nephrotic Syndrome Study Network (NEPTUNE) [J]. Clin J Am Soc Nephrol, 2013, 8(8): 1449-1459.
|
[9] |
Rosenberg AZ, Palmer M, Merlino L, et al. The application of digital pathology to improve accuracy in glomerular enumeration in renal biopsies [J]. PLoS One, 2016, 11(6): e0156441.
|
[10] |
Zee J, Hodgin JB, Mariani LH, et al. Reproducibility and feasibility of strategies for morphologic assessment of renal biopsies using the nephrotic syndrome study network digital pathology scoring system [J]. Arch Pathol Lab Med, 2018, 142(5): 613-625.
|
[11] |
Royal V, Zee J, Liu Q, et al. Ultrastructural characterization of proteinuric patients predicts clinical outcomes [J]. J Am Soc Nephrol, 2020, 31(4): 841-854.
|
[12] |
Mariani LH, Martini S, Barisoni L, et al. Interstitial fibrosis scored on whole-slide digital imaging of kidney biopsies is a predictor of outcome in proteinuric glomerulopathies [J]. Nephrol Dial Transplant, 2018, 33(2): 310-318.
|
[13] |
Simon O, Yacoub R, Jain S, et al. Multi-radial LBP features as a tool for rapid glomerular detection and assessment in whole slide histopathology images [J]. Sci Rep, 2018, 8(1): 2032.
|
[14] |
Bukowy JD, Dayton A, Cloutier D, et al. Region-based convolutional neural nets for localization of glomeruli in trichrome-stained whole kidney sections [J]. J Am Soc Nephrol, 2018, 29(8): 2081-2088.
|
[15] |
Kolachalama VB, Singh P, Lin CQ, et al. Association of pathological fibrosis with renal survival using deep neural networks [J]. Kidney Int Rep, 2018, 3(2): 464-475.
|