Systems and methods for training a statistical model to predict tissue characteristics for a pathology image
In some aspects, the described systems and methods provide for a method for training a statistical model to predict tissue characteristics for a pathology image. The method includes accessing annotated pathology images. Each of the images includes an annotation describing a tissue characteristic category for a portion of the image. A set of training patches and a corresponding set of annotations are defined using an annotated pathology image. Each of the training patches in the set includes values obtained from a respective subset of pixels in the annotated pathology image and is associated with a corresponding patch annotation determined based on an annotation associated with the respective subset of pixels. The statistical model is trained based on the set of training patches and the corresponding set of patch annotations. The trained statistical model is stored on at least one storage device.
1. A method for predicting an entity of interest for a pathology image, the method comprising, by one or more processors:
accessing a pathology image of a patient;
processing, using a trained deep learning model trained on a plurality of annotated images, the pathology image to determine tissue and cell characteristics for the pathology image;
extracting values for one or more features based on the tissue and cell characteristics for the pathology image;
predicting an entity of interest using the values for the one or more features, wherein the entity of interest includes one or more of patient response, tumor molecular characteristics, and patient clinical outcomes; and
outputting the predicted entity of interest associated with the patient for storing on at least one storage device.
2. The method of claim 1 , wherein processing, using the trained deep learning model, the pathology to determine the tissue and cell characteristics comprises:
using a first deep learning model to identify cells characteristics for the pathology image; and
using a second deep learning model to identify tissue characteristics for the pathology image.
3. The method of claim 1 , wherein the pathology image is a H&E whole slide image.
4. The method of claim 1 , wherein the predicted entity of interest comprises response to drug or therapy associated with the patient.
5. The method of claim 1 , wherein the predicted entity of interest comprises prognosis of cancer for the patient.
6. The method of claim 1 , wherein the predicted entity of interest comprises tumor molecular characteristics including genomic markers.
7. The method of claim 1 , wherein the values for one or more features comprise spatial features related to spatial distribution of cells, heterogeneity, and texture.
8. The method of claim 1 , wherein predicting the entity of interest is performed additionally using clinical metadata associated with the patient.
9. The method of claim 1 , wherein the plurality of annotated pathology images are annotated by human pathologists.
10. The method of claim 1 , further comprising displaying one or more annotations overlaid on the pathology image, the one or more annotations describing the determined tissue and cell characteristics for the pathology image.
11. The method of claim 1 , wherein outputting the predicted entity of interest comprises displaying a value indicating a measurement of patient response.
12. A system for predicting an entity of interest for a pathology image, the system comprising:
at least one computer hardware processor; and
at least one non-transitory computer-readable storage medium storing processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
accessing a pathology image of a patient;
processing, using a trained deep learning model trained on a plurality of annotated images, the pathology image to determine tissue and cell characteristics for the pathology image;
extracting values for one or more features based on the tissue and cell characteristics for the pathology image;
predicting an entity of interest using the values for the one or more features, wherein the entity of interest includes one or more of patient response, tumor molecular characteristics, and patient clinical outcomes; and
outputting the predicted entity of interest associated with the patient for storing on at least one storage device.
13. The system of claim 12 , wherein processing, using the trained deep learning model, the pathology to determine the tissue and cell characteristics comprises:
using a first deep learning model to identify cells characteristics for the pathology image; and
using a second deep learning model to identify tissue characteristics for the pathology image.
14. The system of claim 12 , wherein the predicted entity of interest comprises response to drug or therapy associated with the patient.
15. The system of claim 12 , wherein the predicted entity of interest comprises prognosis of cancer for the patient.
16. The system of claim 12 , wherein the predicted entity of interest comprises tumor molecular characteristics including genomic markers.
17. The system of claim 12 , wherein the values for one or more features comprise spatial features related to spatial distribution of cells, heterogeneity, and texture.
18. The system of claim 12 , wherein predicting the entity of interest is performed additionally using clinical metadata associated with the patient.
19. The system of claim 12 , wherein the at least one computer hardware processor is further configured to perform:
causing to display one or more annotations overlaid on the pathology image, the one or more annotations describing the determined tissue and cell characteristics for the pathology image.
20. The system of claim 12 , wherein outputting the predicted entity of interest comprises causing to display a value indicating a measurement of patient response.
21. A non-transitory computer-readable medium containing instructions that, when executed, cause at least one computer hardware processor to perform:
accessing a pathology image of a patient;
processing, using a trained deep learning model trained on a plurality of annotated images, the pathology image to determine tissue and cell characteristics for the pathology image;
extracting values for one or more features based on the tissue and cell characteristics for the pathology image;
predicting an entity of interest using the values for the one or more features, wherein the entity of interest includes one or more of patient response, tumor molecular characteristics, and patient clinical outcomes; and
outputting the predicted entity of interest associated with the patient for storing on at least one storage device.
22. The non-transitory computer-readable medium of claim 21 , wherein processing, using the trained deep learning model, the pathology to determine the tissue and cell characteristics comprises:
using a first deep learning model to identify cells characteristics for the pathology image; and
using a second deep learning model to identify tissue characteristics for the pathology image.
23. The non-transitory computer-readable medium of claim 21 , wherein the predicted entity of interest comprises response to drug or therapy associated with the patient.
24. The non-transitory computer-readable medium of claim 21 , wherein the predicted entity of interest comprises prognosis of cancer for the patient.
25. The non-transitory computer-readable medium of claim 21 , wherein the predicted entity of interest comprises tumor molecular characteristics including genomic markers.
26. The non-transitory computer-readable medium of claim 21 , wherein the plurality of annotated pathology images are annotated by human pathologists.