Systems and methods to process electronic images to determine salient information in digital pathology

1. A computer-implemented method for identifying a diagnostic feature of a digitized pathology image, the method comprising:

receiving one or more digitized images of a pathology specimen, and medical metadata comprising image metadata, specimen metadata, clinical information, and patient information;

applying a machine learning model to generate one or more predictions based on a presence of one or more pathological conditions in the one or more digitized images;

generating, by the machine learning model, at least one relevant diagnostic feature based on one or more of the image metadata, specimen metadata, or clinical information for output to a display, the at least one relevant diagnostic feature being based on the presence of a region having the one or more pathological conditions beyond a statistical likelihood; and

providing, by the machine learning model, visualization of a foci of interest of the at least one relevant diagnostic features for output to a display, the display including one or more indications of locations of the one or more digitized images being associated with one or more pathological conditions beyond a predetermined value.

2. The computer-implemented method of claim 1 , wherein the generating at least one relevant diagnostic feature further comprises:

determining a probability of diagnostic relevance for each of the relevant diagnostic features; and

determining a highest probability among the relevant diagnostic features for output to the display.

3. The computer-implemented method of claim 1 , wherein the generating at least one relevant diagnostic feature further comprises:

determining at least one probability of diagnostic relevance that exceeds a predetermined value among the relevant diagnostic features for output to the display.

4. The computer-implemented method of claim 1 , wherein the generating at least one relevant diagnostic feature further comprises:

determining a predetermined number of highest probabilities of diagnostic relevance among the relevant diagnostic features for output to the display.

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

determining a ranking of probabilities of diagnostic relevance of each of the relevant diagnostic features;

automatically focusing the display on each of the relevant diagnostic features in order based upon the determining of the ranking of probabilities of diagnostic relevance; and

receiving a user input to modify a focus of the display from a first relevant diagnostic feature to a second relevant diagnostic feature.

6. The computer-implemented method of claim 1 , wherein the foci of interest is indicated on a digitized pathology image.

7. The computer-implemented method of claim 6 , wherein the at least one foci of interest is indicated by an outline, a set of crosshairs, or a text descriptor.

8. The computer-implemented method of claim 1 , wherein the method further comprises storing a collection of data into a digital storage device.

9. The computer-implemented method of claim 1 , wherein the method further comprises generating a probability for biomarkers, cancer, and/or histological features on all points of a whole slide image.

10. The computer-implemented method of claim 1 , wherein the method further comprises generating a binary output to indicate whether or not a target feature is present in a selected region.

11. The computer-implemented method of claim 10 , wherein the method further comprises identifying a set of relevant areas where the one or more pathological conditions is present in a whole slide image.

12. The computer-implemented method of claim 11 , wherein the method further comprises computing an overall score for each pathological condition.

13. A system for identifying a diagnostic feature of a digitized pathology image, 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 digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information and/or patient information;

applying a machine learning model to generate one or more predictions based on a presence of one or more pathological conditions in the one or more digitized images;

generating, by the machine learning model, at least one relevant diagnostic feature based on one or more of the image metadata, specimen metadata, or clinical information for output to a display, the at least one relevant diagnostic feature being based on the presence of a region having the one or more pathological conditions beyond a statistical likelihood; and

providing, by the machine learning model, visualization of a foci of interest of the at least one relevant diagnostic features for output to a display, the display including one or more indications of locations of the one or more digitized images being associated with one or more pathological conditions beyond a predetermined value.

14. The system of claim 13 , wherein the generating at least one relevant diagnostic feature further comprises:

determining a probability of diagnostic relevance for each of the relevant diagnostic features; and

determining a highest probability among the relevant diagnostic features for output to the display.

15. The system of claim 13 , wherein the generating of at least one relevant diagnostic feature further comprises:

determining at least one probability of diagnostic relevance that exceeds a predetermined value among the relevant diagnostic features for output to the display.

16. The system of claim 13 , wherein the generating at least one relevant feature further comprises:

determining a predetermined number of highest probabilities of diagnostic relevance among the relevant diagnostic features for output to the display.

17. The system of claim 13 , further comprising:

determining a ranking of probabilities of diagnostic relevance of each of the relevant diagnostic features;

automatically focusing the display on each of the relevant diagnostic features in order based upon the determining of the ranking of probabilities of diagnostic relevance; and

receiving a user input to modify a focus of the display from a first relevant diagnostic feature to a second relevant diagnostic feature.

18. The system of claim 13 , wherein the foci of interest is indicated on a digitized pathology image.

19. The system of claim 18 , wherein the at least one foci of interest is indicated by an outline, a set of crosshairs, or a text descriptor.

20. A non-transitory computer-readable medium storing instructions that, when executed by a processor to perform operations for identifying a diagnostic feature of a digitized pathology image, the operations comprising:

receiving one or more digitized images of a pathology specimen, and medical metadata comprising at least one of image metadata, specimen metadata, clinical information and/or patient information;

applying a machine learning model to generate one or more predictions based on a presence of one or more pathological conditions in the one or more digitized images;

generating, by the machine learning model, at least one relevant diagnostic feature based on one or more of the image metadata, specimen metadata, or clinical information for output to a display, the at least one relevant diagnostic feature being based on the presence of a region having the one or more pathological conditions beyond a statistical likelihood; and

providing, by the machine learning model, visualization of a foci of interest of the at least one relevant diagnostic feature for output to a display, the display including one or more indications of locations of the one or more digitized images being associated with one or more pathological conditions beyond a predetermined value.