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Authors
Li, Xingyu
Jonnagaddala, Jitendra
Yang, Shuhua
Zhang, Hong
Xu, Xu Steven https://orcid.org/0000-0001-6997-5533
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Funding
Funding for this research was provided by:
National Natural Science Foundation of China (11771096, 72091212, 12171451)
Anhui Center for Applied Mathematics, and Special Project of Strategic Leading Science and Technology of CAS (No.XDC08010100)
Australian National Health and Medical Research Council (No.GNT1192469)
Google Cloud Research (No.GCP19980904)
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Article History
Received: 12 December 2021
Accepted: 2 March 2022
First Online: 24 March 2022
Declarations
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: The authors declare no potential conflicts of interest.
: All statistical analysis were conducted in R (version 4.1.0) unless otherwise specified. The following libraries were used: survminer (version 0.4.9), survival (version 3.2–13), and ggplot2 (version 3.3.5). The U-Net model, tissue classifier, risk group predictor were trained with Python (version 3.7.9), Tf-nightly-gpu (version 2.5.0.dev20210209), scipy (version 1.6.1), scikit-learn (version 0.24.1), openslide-python (version 1.1.2), opencv-python (version 4.5.1.48), numpy (version 1.19.5), numba (version 0.52.0), matplotlib (version 3.3.4), pandas (version 1.2.2), and torchvision (version 0.8.2). All training parameters were provided in the source code available at . Source code is available at .
