Systems and methods for processing electronic images of slides for a digital pathology workflow

1. A computer-implemented method of categorizing a sample in digital pathology, comprising:

determining a trained machine learning model, the trained machine learning model having been trained using training data comprising a plurality of training digital pathology images, an identification of cells in each training digital pathology image as being cells of interest or background, and/or an identification of cells in each training digital pathology image as needing further analysis, such that the trained machine learning model is trained to determine features of digital pathology images;

receiving a case associated with digital images of a pathology specimen and comprising a plurality of parts, at a digital storage device;

determining, using the trained machine learning model, a plurality of features based on the digital images associated with the case;

automatically aggregating, using the trained machine learning model, a plurality of portions of the pathology specimen into a part index comprising a part overview generated by the machine learning model and triage information generated by the machine learning model;

determining a diagnosis based on the determined plurality of features, the diagnosis including the part index; and

generating a report including the diagnosis.

2. The computer-implemented method of claim 1 , wherein identifying a case as ready to view comprises verifying that all slides within the case are processed and uploaded to a digital storage device.

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

determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious;

receiving a selection of a part of the plurality of parts;

determining whether a plurality of slides associated with the part are suspicious or non-suspicious;

determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and

annotating the collection of suspicious slides and/or generating the report based on the collection of suspicious slides.

4. The computer-implemented method of claim 3 , wherein annotating the collection of suspicious slides further comprises:

outlining at least one region around suspicious tissue;

measuring a length and/or an area of the suspicious tissue; and

outputting an annotation onto the collection of suspicious slides.

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

populating the report with information about the collection of suspicious slides; and

outputting the report to a user.

6. The computer-implemented method of claim 5 , wherein information about the collection of suspicious slides comprises a focus area, a contextual area, one or more measurements of suspicious tissue, and/or an alphanumeric output.

7. The computer-implemented method of claim 6 , wherein the alphanumeric output comprises a binary indication of a presence of one or more biomarkers within the tissue.

8. The computer-implemented method of claim 5 , wherein the report of a suspicious tissue comprises a detection panel, a quantification panel, and/or an annotation log.

9. The computer-implemented method of claim 8 , wherein the annotation log is searchable at a case level and at a part level.

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

determining a complexity associated with the case; and

prioritizing the case based on the determined complexity.

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

sorting and/or filtering for display, using the machine learning model, the plurality of parts based on the determination of whether the plurality of parts are suspicious or non-suspicious.

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

determining a pathology type; and

generating the report based on the determined pathology type.

13. A system for categorizing a sample in digital pathology, the system comprising:

at least one memory storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

determining a trained machine learning model, the trained machine learning model having been trained using training data comprising a plurality of training digital pathology images, an identification of cells in each training digital pathology image as being cells of interest or background, and/or an identification of cells in each training digital pathology image as needing further analysis, such that the trained machine learning model is trained to determine features of digital pathology images;

receiving a case associated with digital images of a pathology specimen and comprising a plurality of parts, at a digital storage device;

determining, using the machine learning model, a plurality of features based on the digital images associated with the case;

automatically aggregating, using the machine learning model, a plurality of portions of the pathology specimen into a part index comprising a part overview generated by the machine learning model and triage information generated by the machine learning model;

determining a diagnosis based on the determined plurality of features, the diagnosis including the part index; and

generating a report including the diagnosis.

14. The system of claim 13 , wherein identifying a case as ready to view comprises verifying that all slides within the case are processed and uploaded to a digital storage device.

15. The system of claim 13 , wherein the operations further comprise:

determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious;

receiving a selection of a part of the plurality of parts;

determining whether a plurality of slides associated with the part are suspicious or non-suspicious;

determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and

annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides.

16. The system of claim 15 , wherein annotating the collection of suspicious slides further comprises:

outlining at least one region around suspicious tissue;

measuring a length and/or an area of the suspicious tissue; and

outputting an annotation onto the collection of suspicious slides.

17. The system of claim 16 , wherein the operations further comprise:

populating the report with information about the collection of suspicious slides; and

outputting the report to a user, wherein information about the collection of suspicious slides comprises a focus area, a contextual area, one or more measurements of the suspicious tissue, and/or an alphanumeric output.

18. The system of claim 13 , wherein the operations further comprise:

determining a complexity associated with the case; and

prioritizing the case based on the determined complexity.

19. A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform a method of categorizing a sample in digital pathology, the method comprising:

determining a trained machine learning model, the trained machine learning model having been trained using training data comprising a plurality of training digital pathology images, an identification of cells in each training digital pathology image as being cells of interest or background, and/or an identification of cells in each training digital pathology image as needing further analysis, such that the trained machine learning model is trained to determine features of digital pathology images;

receiving a case associated with digital images of a pathology specimen and comprising a plurality of parts, at a digital storage device;

determining, using the machine learning model, a plurality of features based on the digital images associated with the case;

automatically aggregating, using the machine learning model, a plurality of portions of the pathology specimen into a part index comprising a part overview generated by the machine learning model and triage information generated by the machine learning model;

determining a diagnosis based on the determined plurality of features, the diagnosis including the part index; and

generating a report including the diagnosis.

20. The non-transitory computer-readable medium of claim 19 , the method further comprising:

determining, using the machine learning model, whether the plurality of parts are suspicious or non-suspicious;

receiving a selection of a part of the plurality of parts;

determining whether a plurality of slides associated with the part are suspicious or non-suspicious;

determining, using the machine learning model, a collection of suspicious slides, of the plurality of slides, the machine learning model having been trained by processing a plurality of training images; and

annotating the collection of suspicious slides and/or generating a report based on the collection of suspicious slides by:

outlining at least one region around suspicious tissue;

measuring a length and/or an area of the suspicious tissue; and

outputting an annotation onto the collection of suspicious slides.