Systems and methods for processing electronic images of slides for a digital pathology workflow
A computer-implemented method of using a machine learning model to categorize a sample in digital pathology may include receiving one or more cases, each associated with digital images of a pathology specimen; identifying, using the machine learning model, a case as ready to view; receiving a selection of the case, the case comprising a plurality of parts; 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.
1. A computer-implemented method of using at least one machine learning model to categorize a sample in digital pathology, comprising:
receiving one or more cases, each associated with digital images of a pathology specimen, at a digital storage device;
identifying, using the machine learning model, a case of the one or more cases as ready to view;
receiving a selection of the case, the case comprising a plurality of parts;
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;
determining a diagnosis based on the determined plurality of features, the diagnosis including the part index;
determining, using the machine learning model, a plurality of partial reports using the plurality of features; and
aggregating, the plurality of partial reports into a report, the 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 and/or visualization 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 each of the one or more cases; and
prioritizing the one or more cases 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 using at least one machine learning model to categorize 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:
receiving one or more cases, each associated with digital images of a pathology specimen, at a digital storage device;
identifying, using the machine learning model, a case of the one or more cases as ready to view;
receiving a selection of the case, the case comprising a plurality of parts;
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;
determining a diagnosis based on the determined plurality of features, the diagnosis including the part index;
determining, using the machine learning model, a plurality of partial reports using the plurality of features; and
aggregating, the plurality of partial reports into a report, the 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 each of the one or more cases; and
prioritizing the one or more cases based on the determined complexity.
19. A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform a method of using at least one machine learning model to output a task-specific prediction, the method comprising:
receiving one or more cases, each associated with digital images of a pathology specimen, at a digital storage device;
identifying, using the machine learning model, a case of the one or more cases as ready to view;
receiving a selection of the case, the case comprising a plurality of parts;
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;
determining a diagnosis based on the determined plurality of features, the diagnosis including the part index;
determining, using the machine learning model, a plurality of partial reports using the plurality of features; and
aggregating, the plurality of partial reports into a report, the 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.