Systems and methods to process electronic images to provide blur robustness

1. A computer-implemented method for processing electronic medical images, the method comprising: receiving a plurality of electronic medical images of a medical specimen; dividing each of the plurality of electronic medical images into a plurality of tiles, each tile of the plurality of tiles being of a predetermined size; determining a plurality of sets of matching tiles, the tiles within each set corresponding to a given region of a plurality of regions of the medical specimen; for each tile of the plurality of sets of matching tiles, determining a blur score corresponding to a level of image blur of the tile; determining whether if a predetermined threshold of tiles in a given location have inadequate blur scores; upon determining that the predetermined threshold is exceeded, ordering a rescan of the corresponding medical image for each set of matching tiles, determining a tile with the blur score indicating the lowest level of blur; determining a composite electronic medical image, the composite electronic medical image comprising a plurality of tiles from each set of matching tiles with the blur score indicating the lowest level of blur; and providing the composite electronic medical image for display.

2. The computer-implemented method of claim 1 , further comprising: determining a total blur score, the total blur score corresponding to an average of the blur score of all tiles within a medical image, wherein if the total blur score is above a threshold value, a rescan of the corresponding medical image is ordered.

3. The computer-implemented method of claim 1 , wherein the blur score is output by a machine learning model that receives the plurality of tiles, the machine learning model applying, to the tiles, one or more weights, biases, and/or layers, and outputting a blur score for each tile.

4. The computer-implemented method of claim 1 , wherein the step of determining a composite electronic medical image is performed using a machine learning model.

5. The computer-implemented method of claim 1 , further comprising: upon determining each tile within a set has a blur score above a threshold value, using at least one conditional generative model to create a higher resolution of the tile.

6. The computer-implemented method of claim 5 , wherein using a conditional generative model comprises inputting a tile with the lowest blur score into a de-blurring machine learning model that applies the tile data to one or more of de-blurring models weight, biases, or layers, and outputs a tile corresponding tile with a lower blur score.

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

providing a corresponding semantic mask indicating which tiles were altered of the composite medical image.

8. A system for processing electronic 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 plurality of electronic medical images of a medical specimen; dividing each of the plurality of electronic medical images into a plurality of tiles, each tile of the plurality of tiles being of a predetermined size; determining a plurality of sets of matching tiles, the tiles within each set corresponding to a given region of a plurality of regions of the medical specimen; for each tile of the plurality of sets of matching tiles, determining a blur score corresponding to a level of image blur of the tile; determining whether if a predetermined threshold of tiles in a given location have inadequate blur scores; upon determining that the predetermined threshold is exceeded, ordering a rescan of the corresponding medical image for each set of matching tiles, determining a tile with the blur score indicating the lowest level of blur; determining a composite electronic medical image, the composite electronic medical image comprising a plurality of tiles from each set of matching tiles with the blur score indicating the lowest level of blur; and providing the composite electronic medical image for display.

9. The system of claim 8 : further comprising: determining a total blur score, the total blur score corresponding to an average of the blur score of all tiles within a medical image, wherein if the total blur score is above a threshold value, a rescan of the corresponding medical image is ordered.

10. The system of claim 8 , wherein the blur score is output by a machine learning model that receives the plurality of tiles, the machine learning model applying to the tiles, one or more weights, biases, and/or layers, and outputting a blur score for each tile.

11. The system of claim 8 , wherein the step of determining a composite electronic medical image is performed using a machine learning model.

12. The system of claim 8 , further comprising: upon determining each tile within a set has a blur score above a threshold value, use using at least one conditional generative model to create a higher resolution of the tile.

13. The system of claim 12 , wherein using a conditional generative model comprises inputting a tile with the lowest blur score into a de-blurring machine learning model that applies the tile data to one or more of De-blurring models weight, biases, or layers, and outputs a tile corresponding tile with a lower blur score.

14. The system of claim 8 , further comprising:

providing a corresponding semantic mask indicating which tiles were altered of the composite medical image.

15. 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 a plurality of electronic medical images of a medical specimen; dividing each of the plurality of electronic medical images into a plurality of tiles, each tile of the plurality of tiles being of a predetermined size; determining a plurality of sets of matching tiles, the tiles within each set corresponding to a given region of a plurality of regions of the medical specimen; for each tile of the plurality of sets of matching tiles, determining a blur score corresponding to a level of image blur of the tile; determining whether if a predetermined threshold of tiles in a given location have inadequate blur scores; upon determining that the predetermined threshold is exceeded, ordering a rescan of the corresponding medical image for each set of matching tiles, determining a tile with the blur score indicating the lowest level of blur; determining a composite electronic medical image, the composite electronic medical image comprising a plurality of tiles from each set of matching tiles with the blur score indicating the lowest level of blur; and providing the composite electronic medical image for display.

16. The non-transitory computer-readable medium of claim 15 , wherein the blur score is output by a machine learning model that receives the plurality of tiles, the machine learning model applying to the tiles, one or more weights, biases, and/or layers, and outputting a blur score for each tile.

17. The non-transitory computer-readable medium of claim 15 , the operations further comprising: providing a corresponding semantic mask indicating which tiles were altered of the composite medical image.