Systems and methods for processing electronic images for ranking loss and grading

1 . A computer-implemented method for processing electronic medical images, comprising:

receiving one or more medical images of at least one pathology specimen associated with a patient;

dividing the one or more medical images into a plurality of tiles;

predicting, using a machine learning system, proportions of each type of cancer sub-category for the plurality of tiles, the machine learning system having been trained by ranking loss, the machine learning system having been trained by:

dividing a plurality of electronic images into tiles; and

for each of the plurality of electronic images:

predicting proportions of any of a plurality of cancer sub-categories;

determining a loss for each less than relation based on ground-truth proportions;

determining a total loss based on the loss for each less than relation;

determining a backward pass; and

updating the machine learning system using the total loss and backward pass; and

determining an overall grade of cancer for the one or more medical images.

2 . The method of claim 1 , wherein the overall grade of cancer is output as a Gleason score prediction.

3 . The method of claim 1 , wherein predicting proportions of each type of cancer includes predicting proportions of each cancer sub-category.

4 . The method of claim 3 , wherein during training, the ranking loss is determined for each cancer sub category.

5 . The method of claim 4 , wherein the total ranking loss of the machine learning system is calculated using a ranking loss formula Lrank=x+1 if x>0 & Lrank=ex if x<0.

6 . The method of claim 5 , wherein the machine learning system is trained using weak labels to learn more granular labels using the rank loss formula.

7 . The method of claim 3 , further comprising:

outputting the proportion of each cancer sub-category.

8 . The method of claim 1 , further comprising:

outputting a segmentation map displaying a location of each cancer sub-category.

9 . A system for processing electronic 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 one or more medical images of at least one pathology specimen associated with a patient;

dividing the one or more medical images into a plurality of tiles;

predicting, using a machine learning system, proportions of each type of cancer sub-category for the plurality of tiles, the machine learning system having been trained by ranking loss, the machine learning system having been trained by:

dividing a plurality of electronic images into tiles; and

for each of the plurality of electronic images:

predicting proportions of any of a plurality of cancer sub-categories;

determining a loss for each less than relation based on ground-truth proportions;

determining a total loss based on the loss for each less than relation;

determining a backward pass; and

updating the machine learning system using the total loss and backward pass; and

determining an overall grade of cancer for the one or more medical images.

10 . The system of claim 9 , wherein the overall grade of cancer is output as a Gleason score prediction.

11 . The system of claim 9 , wherein predicting proportions of each type of cancer includes predicting proportions of each cancer sub-category.

12 . The system of claim 11 , wherein during training, the ranking loss is determined for each cancer sub category.

13 . The system of claim 12 , wherein the total ranking loss of the machine learning system is calculated using a ranking loss formula Lrank=x+1 if x>0 & Lrank=ex if x<0.

14 . The system of claim 13 , wherein the machine learning system is trained using weak labels to learn more granular labels using the rank loss formula.

15 . The system of claim 11 , further comprising:

outputting the proportion of each cancer sub-category.

16 . The system of claim 9 , further comprising:

outputting a segmentation map displaying a location of each cancer sub-category.

17 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations processing electronic digital medical images, the operations comprising:

receiving one or more medical images of at least one pathology specimen associated with a patient;

dividing the one or more medical images into a plurality of tiles;

predicting, using a machine learning system, proportions of each type of cancer sub-category for the plurality of tiles, the machine learning system having been trained by ranking loss, the machine learning system having been trained by:

dividing a plurality of electronic images into tiles; and

for each of the plurality of electronic images:

predicting proportions of any of a plurality of cancer sub-categories;

determining a loss for each less than relation based on ground-truth proportions;

determining a total loss based on the loss for each less than relation;

determining a backward pass; and

updating the machine learning system using the total loss and backward pass; and

determining an overall grade of cancer for the one or more medical images.

18 . The non-transitory computer-readable medium of claim 17 , wherein the overall grade of cancer is output as a Gleason score prediction.

19 . The non-transitory computer-readable medium of claim 17 , wherein predicting proportions of each type of cancer includes predicting proportions of each cancer sub-category.

20 . The non-transitory computer-readable medium of claim 17 , wherein during training, the ranking loss is determined for each cancer sub category.