CPC G01N 15/1434 (2013.01) [G01N 15/147 (2013.01); G01N 15/1429 (2013.01); G01N 15/1475 (2013.01); G06F 18/214 (2023.01); G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/40 (2022.01); G06V 20/64 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G16B 40/20 (2019.02); G01N 2015/1006 (2013.01); G01N 2015/1445 (2013.01); G06T 2207/10101 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01)] | 29 Claims |
1. A classification training method for training classifiers adapted to identify specific mutations associated with cancer, the method comprising:
identifying 3D image feature data from a plurality of first cells;
generating a first set of 3D cell imaging data from the plurality of first cells with a plurality of known driver mutations and from a plurality of other malignant cells where the plurality of driver mutations is expected to occur, where the first set of cell imaging data includes a plurality of first individual cell images;
generating a second set of 3D cell imaging data from a set of normal cells where the plurality of driver mutations is not expected to occur, where the second set of cell imaging data includes a plurality of second individual cell images;
operating supervised learning based on cell line status as ground truth; and
generating a classifier from the supervised learning.
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