MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors-Reference-Cited by-同舟云学术

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MorphSet: Improving Renal Histopathology Case Assessment Through Learned Prognostic Vectors

Published:2021 Issue: Volume: Page:319-328
ISSN:0302-9743
Container-title:Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
language:
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Author:

Cicalese Pietro Antonio,Rizvi Syed Asad,Wang Victor,Patibandla Sai,Yuan Pengyu,Zare Samira,Moos Katharina,Batal Ibrahim,Clahsen-van Groningen Marian,Roufosse Candice,Becker Jan,Mohan Chandra,Nguyen Hien Van

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-87237-3_31

Reference16 articles.

1. Labelbox, labelbox, online (2020). https://labelbox.com

2. Bankhead, P., et al.: QuPath: open source software for digital pathology image analysis. bioRxiv (2017). https://doi.org/10.1101/099796, https://www.biorxiv.org/content/early/2017/03/06/099796

3. Barisoni, L., et al.: Digital pathology imaging as a novel platform for standardization and globalization of quantitative nephropathology. Clin. Kidney J. 10(2), 176–187 (2017). https://doi.org/10.1093/ckj/sfw129

4. Cicalese, P.A., Mobiny, A., Shahmoradi, Z., Yi, X., Mohan, C., Van Nguyen, H.: Kidney level lupus nephritis classification using uncertainty guided Bayesian convolutional neural networks. IEEE J. Biomed. Health Inform. 25(2), 315–324 (2021). https://doi.org/10.1109/JBHI.2020.3039162

5. Haas, M., et al.: Consensus definitions for glomerular lesions by light and electron microscopy: recommendations from a working group of the Renal Pathology Society. Kidney Int. 98(5), 1120–1134 (2020). https://doi.org/10.1016/j.kint.2020.08.006

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