Systems and methods for processing images to classify the processed images for digital pathology
Systems and methods are disclosed for 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.
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 system 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 system 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;
outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image;
identifying a quality score for the target image, the quality score being determined according to the machine learning system;
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 a quality 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:
outputting the quality score.
6. The computer-implemented method of claim 1 , wherein the recommendation comprises any one or any combination of a specimen cut, a scanning parameter, a slide reconstruction, and a slide marking.
7. The computer-implemented method of claim 1 , further comprising:
determining, using the target image and the machine learning system, 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.
8. A system for analyzing an image corresponding to a specimen, the system comprising:
at least one memory storing instructions; and
at least one processor executing the instructions to perform operations comprising:
receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient;
applying a machine learning system 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 system 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;
outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image;
identifying a quality score for the target image, the quality score being determined according to the machine learning system;
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 a quality of the target image.
9. The system of claim 8 , the operations 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.
10. The system of claim 8 , the operations 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.
11. The system of claim 8 , the operations 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.
12. The system of claim 8 , the operations further comprising:
outputting the quality score.
13. The system of claim 8 , wherein the recommendation comprises any one or any combination of a specimen cut, a scanning parameter, a slide reconstruction, and a slide marking.
14. The system of claim 8 , the operations further comprising:
determining, using the target image and the machine learning system, 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.
15. 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 system 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 system 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;
outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image; and
determining, using the target image and the machine learning system, whether the target specimen is post-treatment or pre-treatment.
16. The computer-implemented method of claim 15 , 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.
17. The computer-implemented method of claim 15 , 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.
18. The computer-implemented method of claim 15 , 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.
19. The computer-implemented method of claim 15 , further comprising:
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.
20. A system for analyzing an image corresponding to a specimen, the system comprising:
at least one memory storing instructions; and
at least one processor executing the instructions to perform operations comprising:
receiving a target image corresponding to a target specimen, the target specimen comprising a tissue sample of a patient;
applying a machine learning system 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 system 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;
outputting the at least one characteristic of the target specimen and/or the at least one characteristic of the target image; and
determining, using the target image and the machine learning system, whether the target specimen is post-treatment or pre-treatment.