SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES

1 - 20 . (canceled)

21 . 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 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.

22 . The image processing method of claim 21 , further comprising:

determining, via a second trained machine learning model, a prioritization value of a plurality of prioritization values for the target image; and

causing to output the target image sorted in a prioritization order based on the prioritization value and the quality control metric.

23 . The image processing method of claim 22 , wherein the second trained machine learning model has been trained by:

receiving, as training data, a plurality of digital medical images associated with a plurality of patients and a prioritization value for each of the plurality of digital medical images; and

training a machine learning model, using the training data, to infer the prioritization value based on the plurality of digital medical images.

24 . The image processing method of claim 21 , further comprising removing the target image from the sequence of the plurality of digitized pathology images based on the determined quality control metric.

25 . The image processing method of claim 21 , wherein the first trained machine learning model has been trained by:

receiving, as training data, a plurality of digital medical images associated with a plurality of patients and the quality control metric for each of the plurality of digital medical images; and

training a machine learning model, using the training data, to infer the quality control metric based on the plurality of digital medical images.

26 . The image processing method of claim 21 , wherein the quality control metric further signifies a severity of the quality control issue.

27 . The image processing method of claim 21 , further comprising:

generating an alert, the alert including the determine quality control metric for the target image; and

outputting, via the user interface, the alert.

28 . The image processing method of claim 27 , further comprising:

determining whether the quality control metric signifies a quality control issue that impacts rendering a diagnosis; and

upon determining the quality control metric signifies that the quality control issue impacts rendering a diagnosis, generating the alert.

29 . The image processing method of claim 27 , further comprising:

determining whether the quality control metric associated with the quality control issue exceeds a predetermined quality control metric threshold value; and

upon determining the quality control metric associated with the quality control issue exceeds the predetermined quality control metric threshold value, generating the alert.

30 . The image processing method of claim 27 , further comprising:

identifying personnel associated with the determined quality control issue; and

outputting the generated alert to a user interface associated with the identified personnel.

31 . 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 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.

32 . The system of claim 31 , the operations further comprising:

determining, via a second trained machine learning model, a prioritization value of a plurality of prioritization values for the target image; and

causing to output the target image sorted in a prioritization order based on the prioritization value and the quality control metric.

33 . The system of claim 32 , wherein the second trained machine learning model has been trained by:

receiving, as training data, a plurality of digital medical images associated with a plurality of patients and a prioritization value for each of the plurality of digital medical images; and

training a machine learning model, using the training data, to infer the prioritization value based on the plurality of digital medical images.

34 . The system of claim 31 , wherein the first trained machine learning model has been trained by:

receiving, as training data, a plurality of digital medical images associated with a plurality of patients and the quality control metric for each of the plurality of digital medical images; and

training a machine learning model, using the training data, to infer the quality control metric based on the plurality of digital medical images.

35 . The system of claim 31 , the operations further comprising:

generating an alert, the alert including the determine quality control metric for the target image; and

outputting, via the user interface, the alert.

36 . The system of claim 35 , the operations further comprising:

determining whether the quality control metric signifies a quality control issue that impacts rendering a diagnosis; and

upon determining the quality control metric signifies that the quality control issue impacts rendering a diagnosis, generating the alert.

37 . The system of claim 35 , the operations further comprising:

determining whether the quality control metric associated with the quality control issue exceeds a predetermined quality control metric threshold value; and

upon determining the quality control metric associated with the quality control issue exceeds the predetermined quality control metric threshold value, generating the alert.

38 . The system of claim 35 , the operations further comprising:

identifying personnel associated with the determined quality control issue; and

outputting the generated alert to a user interface associated with the identified personnel.

39 . 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 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.

40 . The non-transitory computer-readable medium of claim 39 , wherein the first trained machine learning model has been trained by:

receiving, as training data, a plurality of digital medical images associated with a plurality of patients and a quality control metric for each of the plurality of digital medical images; and

training a machine learning model, using the training data, to infer the quality control metric based on the plurality of digital medical images.