CPC G06V 20/69 (2022.01) [G06F 18/10 (2023.01); G06F 18/2135 (2023.01); G06F 18/2148 (2023.01); G06F 18/241 (2023.01); G06F 18/2415 (2023.01); G06T 7/0012 (2013.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30101 (2013.01); G06V 40/14 (2022.01); G06V 2201/031 (2022.01)] | 20 Claims |
1. A method comprising:
obtaining a high-resolution whole slide image of a post-birth placenta;
analyzing the whole slide image, using a trained machine learning model, to identify one or more blood vessels in the placenta, the analysis occurring at a lower resolution than the native resolution of the whole slide image;
classifying the identified blood vessels at a higher resolution using a trained machine learning classifier that outputs a latent vector for each classified blood vessel:
aggregating the latent vectors for a predetermined number of classified blood vessels;
pooling the aggregated latent vectors by calculating a maximum or minimum of the data for each node of a feature map of the machine learning classifier;
reducing the dimension on the pooled aggregated latent vectors to produce a reduced dimension latent vector; and
performing a binary classification for each whole slide image based on the latent vector.
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