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 each tile within a first set of matching tiles has a blur score beyond a threshold value;

upon determining that each tile within a first set of matching tiles has a blur score beyond a threshold value, applying at least one generative model to at least one tile of the first set of matching tiles to generate a higher resolution tile;

for each set of the plurality of sets of matching tiles, determining a tile with the blur score indicating a 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 if a predetermined threshold of tiles in a given location have inadequate blur scores; and

upon determining that the predetermined threshold of tiles in a given location have inadequate blur scores, ordering a rescan of the corresponding medical image.

3 . The computer-implemented method of claim 1 , wherein determining a blur score further comprises:

determining whether each tile within the first set of matching tiles has a blur score beyond a second threshold value; and

upon determining whether each tile within the first set of matching tiles has a blur score beyond the second threshold value, indicating or storing an indication that a rescan of the medical specimen is needed.

4 . 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.

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

6 . The computer-implemented method of claim 1 , wherein the blur score is determined by applying a wavelet transform for each tile of the plurality of sets of matching tiles.

7 . The computer-implemented method of claim 1 , wherein the blur score is determined by applying a Discrete Fourier Transform to each tile of the plurality of sets of matching tiles.

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

providing at least one corresponding indication of which tiles of the composite electronic medical image had the at least one generative model applied.

9 . 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 each tile within a first set of matching tiles has a blur score beyond a threshold value;

upon determining that each tile within a first set of matching tiles has a blur score beyond a threshold value, applying at least one generative model to at least one tile of the first set of matching tiles to generate a higher resolution tile;

for each set of the plurality of sets of matching tiles, determining a tile with the blur score indicating a 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.

10 . The system of claim 9 , further comprising:

determining if a predetermined threshold of tiles in a given location have inadequate blur scores; and

upon determining that the predetermined threshold of tiles in a given location have inadequate blur scores, ordering a rescan of the corresponding medical image.

11 . The system of claim 9 , wherein determining a blur score further comprises:

determining whether each tile within the first set of matching tiles has a blur score beyond a second threshold value; and

upon determining whether each tile within the first set of matching tiles has a blur score beyond the second threshold value, indicating or storing an indication that a rescan of the medical specimen is needed.

12 . The system of claim 9 , 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.

13 . The system of claim 9 , wherein determining a composite electronic medical image is performed using a machine learning model.

14 . The system of claim 9 , wherein the blur score is determined by applying a wavelet transform for each tile of the plurality of sets of matching tiles.

15 . The system of claim 9 , wherein the blur score is determined by applying a Discrete Fourier Transform to each tile of the plurality of sets of matching tiles.

16 . The system of claim 9 , further comprising:

providing at least one corresponding indication of which tiles of the composite electronic medical image had the at least one generative model applied.

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 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 each tile within a first set of matching tiles has a blur score beyond a threshold value;

upon determining that each tile within a first set of matching tiles has a blur score beyond a threshold value, applying at least one generative model to at least one tile of the first set of matching tiles to generate a higher resolution tile;

for each set of the plurality of sets of matching tiles, determining a tile with the blur score indicating a 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.

18 . The computer-readable medium of claim 17 , further comprising:

determining if a predetermined threshold of tiles in a given location have inadequate blur scores; and

upon determining that the predetermined threshold of tiles in a given location have inadequate blur scores, ordering a rescan of the corresponding medical image.

19 . The computer-readable medium of claim 17 , 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.

20 . The computer-readable medium of claim 17 , the operations further comprising:

providing at least one corresponding indication of which tiles of the composite electronic medical image had the at least one generative model applied.