SYSTEMS AND METHODS FOR PROCESSING IMAGES TO CLASSIFY THE PROCESSED IMAGES FOR DIGITAL PATHOLOGY

1 . A computer-implemented method for analyzing an image corresponding to a specimen, the method comprising:

receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient;

applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated; and

outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

2 . The computer-implemented method of claim 1 , further comprising:

determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target specimen; and

outputting the prediction of the specimen type of the target specimen.

3 . The computer-implemented method of claim 1 , further comprising:

determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target specimen; and

in response to determining that a confidence value of the prediction does not exceed a predetermined threshold, outputting an alert indicating that the specimen type of the target specimen is not identifiable.

4 . The computer-implemented method of claim 1 , further comprising:

determining a confidence value of a prediction of a specimen type of the target specimen based on the at least one characteristic of the target specimen; and

outputting the confidence value.

5 . The computer-implemented method of claim 1 , further comprising:

identifying a quality score for the target image, the quality score being determined according to the machine learning model; and

outputting the quality score.

6 . The computer-implemented method of claim 1 , further comprising:

identifying a quality score for the target image, the quality score being determined according to the machine learning model;

determining whether the quality score for the target image is less than a predetermined value; and

in response to the quality score for the target image being less than the predetermined value, outputting a recommendation for increasing the quality score for the target image.

7 . The computer-implemented method of claim 6 , wherein the recommendation comprises any one or any combination of a specimen cut, a scanning parameter, a slide reconstruction, and a slide marking.

8 . The computer-implemented method of claim 1 , further comprising:

determining, using the target image and the machine learning model, whether the target specimen is post-treatment or pre-treatment.

9 . The computer-implemented method of claim 1 , further comprising:

determining, using the target image and the machine learning model, whether the target specimen is post-treatment or pre-treatment;

upon determining that the target specimen is post-treatment, determining a predicted degree to which the target specimen has been treated based on the target image; and

outputting the predicted degree to which the target specimen has been treated.

10 . A system for analyzing an image corresponding to a specimen, the system comprising:

a memory storing instructions; and

a processor executing the instructions to perform a process including:

receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient;

applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated; and

outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

11 . The system of claim 10 , further comprising:

determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target specimen; and

outputting the prediction of the specimen type of the target specimen.

12 . The system of claim 10 , further comprising:

determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target specimen; and

in response to determining that a confidence value of the prediction does not exceed a predetermined threshold, outputting an alert indicating that the specimen type of the target specimen is not identifiable.

13 . The system of claim 10 , further comprising:

determining a confidence value of a prediction of a specimen type of the target specimen based on the at least one characteristic of the target specimen; and

outputting the confidence value.

14 . The system of claim 10 , further comprising:

identifying a quality score for the target image, the quality score being determined according to the machine learning model; and

outputting the quality score.

15 . The system of claim 10 , further comprising:

identifying a quality score for the target image, the quality score being determined according to the machine learning model;

determining whether the quality score for the target image is less than a predetermined value; and

in response to the quality score for the target image being less than the predetermined value, outputting a recommendation for increasing the quality score for the target image.

16 . The system of claim 15 , wherein the recommendation comprises any one or any combination of a specimen cut, a scanning parameter, a slide reconstruction, and a slide marking.

17 . The system of claim 10 , further comprising:

determining, using the target image and the machine learning model, whether the target specimen is post-treatment or pre-treatment.

18 . The system of claim 10 , further comprising:

determining, using the target image and the machine learning model, whether the target specimen is post-treatment or pre-treatment;

upon determining that the target specimen is post-treatment, determining a predicted degree to which the target specimen has been treated based on the target image; and

outputting the predicted degree to which the target specimen has been treated.

19 . A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform a method for analyzing an image corresponding to a specimen, the method comprising:

receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient;

applying a machine learning model to the target image to determine at least one characteristic of the target specimen and/or at least one characteristic of the target image, the machine learning model having been generated by processing a plurality of training images to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated; and

outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image.

20 . The non-transitory computer-readable medium of claim 19 , further comprising:

determining a prediction of a specimen type of the target specimen based on the at least one characteristic of the target specimen; and

outputting the prediction of the specimen type of the target specimen.