SYSTEMS AND METHODS FOR PROCESSING ELECTRONIC IMAGES FOR COMPUTATIONAL ASSESSMENT OF DISEASE

1 - 20 . (canceled)

21 . A computer-implemented method for processing electronic images, the method comprising:

receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen;

determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further output a cancer quantification if the cancer qualification is an confirmed cancer qualification;

providing the digital image as an input to the detection machine learning model;

receiving a confirmed cancer quantification comprising of a minimal residual disease (MRD) as an output from the detection machine learning model, wherein the detection machine learning model comprises a treatment effect machine learning model, and wherein the treatment effect machine learning model is initialized with one of weights and/or layers from a trained version of the detection machine learning model; and

outputting the MRD cancer qualification based on the treatment effects machine learning model.

22 . The computer-implemented method of claim 21 , further including receiving one of a pathological complete response (pCR) cancer qualification.

23 . The computer-implemented method of claim 21 , wherein the MRD cancer qualification is protocol specific.

24 . The computer-implemented method of claim 21 , wherein the MRD cancer qualification corresponds to a number of cancer cells below a MRD threshold.

25 . The computer-implemented method of claim 21 ,wherein the MRD cancer qualification identifies one or more diseases that remains occult within the patient, but may eventually lead to a relapse.

26 . The computer-implemented method of claim 21 , wherein the treatment effect machine learning model is trained based on tagged treatment effects in the plurality of training images.

27 . The computer-implemented method of claim 21 , wherein receiving the confirmed cancer quantification also comprises receiving a type of cancer when the output of the detection machine learning model comprises a confirmed cancer quantification

28 . The computer-implemented method of claim 27 , wherein the type of cancer is determined based on the digital image and one or more of a tissue characteristics, slide type, glass type, tissue type, tissue region, chemical used, or stain amount.

29 . The computer-implemented method of claim 1 , wherein the digital image is from a pathology category, the pathology category selected from one or more of histology, cytology, frozen section, immunohistochemistry (IHC), immunofluorescence, hematoxylin and eosin (H&E), hematoxylin alone, molecular pathology, and/or 3D imaging.

30 . A system for processing electronic images, the system comprising:

at least one memory storing instructions; and

at least one processor executing the instructions to perform operations comprising:

receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen;

determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further output a cancer quantification if the cancer qualification is a confirmed cancer qualification;

providing the digital image as an input to the detection machine learning model;

receiving a confirmed cancer quantification comprising of a minimal residual disease (MRD) as an output from the detection machine learning model, wherein the detection machine learning model comprises a treatment effect machine learning model, and wherein the treatment effect machine learning model is initialized with one of weights and/or layers from a trained version of the detection machine learning model; and

outputting the MRD cancer qualification based on the treatment effects machine learning model.

31 . The system of claim 30 , further including receiving one of a pathological complete response (pCR) cancer qualification.

32 . The system of claim 30 , wherein the MRD cancer qualification is protocol specific.

33 . The system of claim 30 , wherein the MRD cancer qualification corresponds to a number of cancer cells below a MRD threshold.

34 . The system of claim 30 , wherein the MRD cancer qualification identifies one or more disease that remains occult within the patient, but may eventually lead to a relapse.

35 . The system of claim 30 , wherein receiving the confirmed cancer quantification also comprises receiving a type of cancer when the output of the detection machine learning model comprises a confirmed cancer quantification.

36 . The system of claim 35 , wherein the type of cancer is determined based on the digital image and one or more of a tissue characteristics, slide type, glass type, tissue type, tissue region, chemical used, or stain amount.

37 . The system of claim 30 , wherein the digital image is from a pathology category, the pathology category selected from one or more of histology, cytology, frozen section, immunohistochemistry (IHC), immunofluorescence, hematoxylin and eosin (H&E), hematoxylin alone, molecular pathology, and/or 3D imaging.

38 . A non-transitory computer-readable medium storing instructions that, when executed by processor, cause the processor to perform operations for processing electronic images, the operations comprising:

receiving a digital image corresponding to a target specimen associated with a pathology category, wherein the digital image is an image of tissue specimen;

determining a detection machine learning model, the detection machine learning model being generated by processing a plurality of training images to output a cancer qualification and further output a cancer quantification if the cancer qualification is a confirmed cancer qualification;

providing the digital image as an input to the detection machine learning model;

receiving a confirmed cancer quantification comprising of a minimal residual disease (MRD) as an output from the detection machine learning model, wherein the detection machine learning model comprises a treatment effect machine learning model, and wherein the treatment effect machine learning model is initialized with one of weights and/or layers from a trained version of the detection machine learning model; and

outputting the MRD cancer qualification based on the treatment effects machine learning model.

39 . The non-transitory computer-readable medium of claim 38 , further including receiving one of a pathological complete response (pCR) cancer qualification.

40 . The non-transitory computer-readable medium of claim 38 , wherein the MRD cancer qualification is protocol specific.