| CPC G06T 7/0012 (2013.01) [G06F 18/2431 (2023.01); G06T 7/174 (2017.01); G06T 7/194 (2017.01); G06V 10/454 (2022.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06V 10/7635 (2022.01); G06V 10/82 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06T 2207/10064 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01)] | 31 Claims |
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1. A method, comprising:
using at least one computer hardware processor to perform:
obtaining at least one multiplexed immunofluorescence (MxIF) image of a tissue sample, wherein the at least one MxIF image comprises a plurality of channels that are associated with respective markers in a plurality of markers;
obtaining, using a machine learning technique, information indicative of locations of cells in the at least one MxIF image;
identifying multiple groups of cells in the at least one MxIF image at least in part by:
identifying pixel values for at least some of the cells using the at least one MxIF image and the information indicative of locations of cells;
determining, using a neural network, marker expression signatures for the at least some of the cells at least in part by using the identified pixel values, wherein each marker expression signature for a particular cell includes, for each particular marker of one or more of the plurality of markers, a respective likelihood output by the neural network that the particular marker is expressed in the particular cell; and
grouping the at least some of the cells into the multiple groups using the marker expression signatures, the grouping performed by using the respective likelihood output by the neural network for each particular marker of the one or more of the plurality of markers; and
determining at least one characteristic of the tissue sample using the multiple groups.
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