scholarly article | Q13442814 |
P356 | DOI | 10.1038/S41598-021-83102-6 |
P2093 | author name string | Johan Lundin | |
Mikael Lundin | |||
Nina Linder | |||
Pirkko-Liisa Kellokumpu-Lehtinen | |||
Stig Nordling | |||
Jorma Isola | |||
Heikki Joensuu | |||
Harri Sihto | |||
Tiina Lehtimäki | |||
Karl von Smitten | |||
Aleksei Tiulpin | |||
Dmitrii Bychkov | |||
Hakan Kücükel | |||
P4510 | describes a project that uses | ImageNet | Q24901201 |
Statsmodels | Q25304899 | ||
PyTorch | Q47509047 | ||
P433 | issue | 1 | |
P407 | language of work or name | English | Q1860 |
P577 | publication date | 2021-02-17 | |
P1433 | published in | Scientific Reports | Q2261792 |
P1476 | title | Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy | |
P478 | volume | 11 |
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