US 12,424,323 B2
Hybrid and accelerated ground-truth generation for duplex arrays
Qinle Ba, San Mateo, CA (US); Jim F. Martin, Mountain View, CA (US); Satarupa Mukherjee, Fremont, CA (US); Yao Nie, Sunnyvale, CA (US); Xiangxue Wang, Cleveland Heights, OH (US); and Mohammadhassan Izady Yazdanabadi, Palo Alto, CA (US)
Assigned to Ventana Medical Systems, Inc., Tucson, AZ (US)
Filed by VENTANA MEDICAL SYSTEMS, INC., Tucson, AZ (US)
Filed on Mar. 22, 2023, as Appl. No. 18/125,043.
Application 18/125,043 is a continuation in part of application No. PCT/US2023/015939, filed on Mar. 22, 2023.
Claims priority of provisional application 63/269,833, filed on Mar. 23, 2022.
Prior Publication US 2023/0307132 A1, Sep. 28, 2023
Int. Cl. G06K 9/00 (2022.01); G06V 10/774 (2022.01); G06V 10/94 (2022.01); G06V 20/69 (2022.01); G06V 20/70 (2022.01); G16H 50/20 (2018.01)
CPC G16H 50/20 (2018.01) [G06V 10/774 (2022.01); G06V 10/945 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06V 20/70 (2022.01); G06V 2201/03 (2022.01)] 19 Claims
OG exemplary drawing
 
13. A system comprising:
one or more data processors; and
a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of operations including:
accessing a digital pathology image that depicts a tissue slice stained with multiple stains, each of the multiple stains staining for a corresponding biomarker of a set of biomarkers, wherein the multiple stains include at least three stains;
generating, using a first machine-learning model, a segmented image that identifies at least:
a predicted diseased region in the digital pathology image; and
a background region in the digital pathology image, wherein the background region indicates that signals that are present within the background region are not to be assessed when analyzing signals of the set of biomarkers;
detecting depictions of a set of cells in the digital pathology image;
generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of the set of biomarkers are expressed in the cell;
detecting that a subset of the set of cells in the digital pathology image are within the background region; and
in response to detecting that the subset of the set of cells in the digital pathology image are within the background region, updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.