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US 12,749,566 B2Granted
Systems and methods for processing electronic images
AssigneePaige.AI, Inc.
InventorsRan Godrich, Jillian Sue, Leo Grady, Thomas Fuchs
Life of this patent
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Abstract
Systems and methods are disclosed for processing images including, for example, receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient; determining a quality control metric for the target image via a first trained machine learning model having been trained to predict the quality control metric based on the target image, wherein the quality control metric signifies a quality control issue; and outputting, via a user interface, a sequence of a plurality of digitized pathology images, wherein a placement of the target image in the sequence is based on the quality control metric.
Claims19
An image processing method, comprising:
- receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient;
- determining, via first trained machine learning model, a predicted quality control metric for the target image, wherein the predicted quality control metric signifies a presence of a quality control issue;
- determining, via a second trained machine learning model, a prioritization value for the target image based on the predicted quality control metric;
- and
- outputting, via a user interface, a sequence of the target image and a plurality of digitized pathology images, wherein a placement of the target image in the sequence is based on the prioritization value of the target image, and wherein outputting the sequence of the target image and the plurality of digitized pathology images comprises:
- determining a sequence of the plurality of digitized pathology images based on a plurality of prioritization values associated with the plurality of digitized pathology images;
- placing the target image within the sequence based on the prioritization value of the target image;
- and
- outputting a display of a visual sorting of the target image and the plurality of digitized pathology images, wherein the visual sorting corresponds to the sequence.
6 more elements
A system for processing digital medical images, the system comprising:
- at least one memory storing instructions;
- and
- at least one processor configured to execute the instructions to perform operations comprising:
- receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient;
- determining, via first trained machine learning model, a predicted quality control metric for the target image, wherein the predicted quality control metric signifies a presence of a quality control issue;
- determining, via a second trained machine learning model, a prioritization value for the target image based on the predicted quality control metric;
- and
- outputting, via a user interface, a sequence of the target image and a plurality of digitized pathology images, wherein a placement of the target image in the sequence is based on the prioritization value of the target image, and wherein outputting the sequence of the target image and the plurality of digitized pathology images comprises:
- determining a sequence of the plurality of digitized pathology images based on a plurality of prioritization values associated with the plurality of digitized pathology images;
- placing the target image within the sequence based on the prioritization value of the target image;
- and
- outputting a display of a visual sorting of the target image and the plurality of digitized pathology images, wherein the visual sorting corresponds to the sequence.
9 more elements
A non-transitory computer-readable medium storing instructions that,
- when executed by at least one processor, cause the at least one processor to perform image processing operations, the operations comprising:
- receiving a target image of a slide corresponding to a target specimen comprising a tissue sample of a patient;
- determining, via first trained machine learning model, a predicted quality control metric for the target image, wherein the predicted quality control metric signifies a presence of a quality control issue;
- determining, via a second trained machine learning model, a prioritization value for the target image based on the predicted quality control metric;
- and
- outputting, via a user interface, a sequence of the target image and a plurality of digitized pathology images, wherein a placement of the target image in the sequence is based on the prioritization value of the target image, and wherein outputting the sequence of the target image and the plurality of digitized pathology images comprises:
- determining a sequence of the plurality of digitized pathology images based on a plurality of prioritization values associated with the plurality of digitized pathology images;
- placing the target image within the sequence based on the prioritization value of the target image;
- and
- outputting a display of a visual sorting of the target image and the plurality of digitized pathology images, wherein the visual sorting corresponds to the sequence.
7 more elements
Dependencies are inferred from the claim text.