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 identify a presence of one or more treatment effects and predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated;

upon identifying the presence of the one or more treatment effects, predicting the one or more treatment effects; and

providing the at least one characteristic of the target specimen and/or the at least one characteristic of the target image for storage and/or display.

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

applying a trained machine learning model to the target image to determine a degree of treatment effects; and

outputting an indication of a degree to which the tissue sample of the patient has been treated.

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

upon determining the target image being classified as treated, outputting a degree of treatment effects associated with the treated target image.

4. The computer-implemented method of claim 1 , wherein the trained machine learning model is trained by:

receiving, as training data, a plurality of training images and one or more treatment effect labels corresponding to each training image of the plurality of training images; and

training a machine learning model, using the training data, to infer one or more of whether a target image contains a treatment effect or a degree of treatment effects.

5. The computer-implemented method of claim 4 , wherein the plurality of training images comprise a plurality of digital pathology images that include images of tissues that have treatment effects and images of tissues that do not have treatment effects.

6. The computer-implemented method of claim 1 , wherein the presence of one or more treatment effects includes one or more prior treatments associated with the tissue sample of the patient.

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

appending a label to the target image, the label indicating the presence of the one or more treatment effects for the target image.

8. The computer-implemented method of claim 7 , further comprising:

providing the target image and appended label to a digital storage system.

9. 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 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 a presence of one or more treatment effects and predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated;

upon identifying the presence of the one or more treatment effects, predicting the one or more treatment effects; and

providing the at least one characteristic of the target specimen and/or the at least one characteristic of the target image for storage and/or display.

10. The system of claim 9 , further comprising:

applying a trained machine learning model to the target image to determine a degree of treatment effects; and

outputting an indication of a degree to which the tissue sample of the patient has been treated.

11. The system of claim 10 , further comprising:

upon determining the target image being classified as treated, outputting a degree of treatment effects associated with the treated target image.

12. The system of claim 9 , wherein the trained machine learning model is trained by:

receiving, as training data, a plurality of training images and one or more treatment effect labels corresponding to each training image of the plurality of training images; and

training a machine learning model, using the training data, to infer one or more of whether a target image contains a treatment effect or a degree of treatment effects.

13. The system of claim 12 , wherein the plurality of training images comprise a plurality of digital pathology images that include images of tissues that have treatment effects and images of tissues that do not have treatment effects.

14. The system of claim 9 , wherein the presence of one or more treatment effects includes one or more prior treatments associated with the tissue sample of the patient.

15. The system of claim 9 , further comprising:

appending a label to the target image, the label indicating the presence of the one or more treatment effects for the target image.

16. The system of claim 15 , further comprising:

providing the target image and appended label to a digital storage system.

17. 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 a degree of treatment effects based on a determined presence of one or more treatment effects, and/or to predict at least one characteristic, the training images comprising images of human tissue and/or images that are algorithmically generated;

in response to classifying the tissue sample as treated, predicting the degree of treatment effects; and

providing the at least one characteristic of the target specimen and/or the at least one characteristic of the target image for storage and/or display.

18. The computer-implemented method of claim 17 , wherein the trained machine learning model is trained by:

receiving, as training data, a plurality of training images, the plurality of training images comprising a plurality of digital pathology images that include images of tissues that have treatment effects and images of tissues that do not have treatment effects;

receiving, as training data, one or more treatment effect labels corresponding to each training image of the plurality of training images; and

training a machine learning model, using the training data, to infer a degree of treatment effects.

19. The computer-implemented method of claim 17 , further comprising:

determining one or more morphological changes to the tissue sample based on the determined treatment effect.

20. The computer-implemented method of claim 17 , further comprising:

appending a label to the target image, the label indicating the presence of the one or more treatment effects for the target image; and

providing the target image and appended label to a digital storage system.