CPC G06T 7/0012 (2013.01) [G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 10/993 (2022.01); G06V 20/69 (2022.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01); G06V 2201/03 (2022.01)] | 20 Claims |
1. A computer-implemented method for processing digital images of a tissue specimen, the method comprising:
receiving, by a floater detection platform, the digital images of the tissue specimen;
receiving, by the floater detection platform, data associated with a tissue type of the tissue specimen, the data including a tissue class;
detecting, by the floater detection platform, one or more contiguous tissue pieces of the tissue specimen using an analysis of the digital images;
extracting, by the floater detection platform, a plurality of features from each contiguous tissue piece of the one or more contiguous tissue pieces of the tissue specimen;
providing, by the floater detection platform, the plurality of features from each contiguous tissue piece to a machine-learning model, wherein the machine-learning model has been trained, using one or more gathered and/or simulated sets of digital images of a plurality of tissue specimens, to identify a sample tissue class of each contiguous tissue piece based on the plurality of features;
outputting, by the machine-learning model, a first sample tissue class of a first contiguous tissue piece;
comparing, by the floater detection platform, the first sample tissue class of the first contiguous tissue piece with the tissue class of the tissue specimen to determine a tissue class mismatch; and
generating, by the floater detection platform, an alert based on the tissue class mismatch.
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