SYSTEMS AND METHODS TO PROCESS ELECTRONIC IMAGES TO PRODUCE A TISSUE MAP VISUALIZATION
Systems and methods are disclosed for analyzing an image of a slide corresponding to a specimen, the method including receiving at least one digitized image of a pathology specimen; determining, using the digitized image at an artificial intelligence (AI) system, at least one salient feature, the at least one salient comprising a biomarker, cancer, cancer grade, parasite, toxicity, inflammation, and/or cancer sub-type; determining, at the AI system, a salient region overlay for the digitized image, wherein the AI system indicates a value for each pixel; and suppressing, based on the value for each pixel, one or more non-salient regions of the digitized image.
1 - 20 . (canceled)
21 . A method of analyzing a pathological slide, the method comprising:
detecting a plurality of tumor cells included in one or more tumor areas from a pathological slide image;
determining a cell expression class of the plurality of tumor cells, based on a biomarker expression degree of the plurality of tumor cells; and
generating a heatmap image for the pathological slide image, based on a result of the determining.
22 . The method of claim 21 , wherein the heatmap image indicates scores and/or probabilities for each pixel in the pathological slide image, the scores and/or probabilities being based on the biomarker expression degree.
23 . The method of claim 21 , wherein the detecting comprises:
identifying one or more tumor areas and a plurality of tumor cells from the pathological slide image by using an artificial intelligence (AI) model; and
identifying a plurality of tumor cells included in the one or more tumor areas from the plurality of identified tumor cells.
24 . The method of claim 23 , wherein the identifying one or more tumor areas comprises identifying a pixel corresponding to the one or more tumor areas by analyzing the pathological slide image in units of pixels.
25 . The method of claim 21 , further comprising:
suppressing a non-diseased region from the heatmap image of the pathological slide.
26 . The method of claim 21 , wherein generating a heatmap image for the pathological slide image further comprises:
determining, based on at least one user input, tissue visualization maps to display in the heatmap.
27 . The method of claim 21 , wherein the heatmap image indicates a value for each pixel based on the biomarker expression degree.
28 . A system for analyzing a pathological slide, the system comprising:
at least one memory storing instructions; and
at least one processor configured to execute the instructions to perform operations comprising:
detecting a plurality of tumor cells included in one or more tumor areas from a pathological slide image;
determining a cell expression class of the plurality of tumor cells, based on a biomarker expression degree of the plurality of tumor cells; and
generating a heatmap image for the pathological slide image, based on a result of the determining.
29 . The system of claim 28 , wherein the heatmap image indicates scores and/or probabilities for each pixel in the pathological slide image, the scores and/or probabilities being based on the biomarker expression degree.
30 . The system of claim 28 , wherein the detecting comprises:
identifying one or more tumor areas and a plurality of tumor cells from the pathological slide image by using an artificial intelligence (AI) model; and
identifying a plurality of tumor cells included in the one or more tumor areas from the plurality of identified tumor cells.
31 . The system of claim 30 , wherein the identifying one or more tumor areas comprises identifying a pixel corresponding to the one or more tumor areas by analyzing the pathological slide image in units of pixels.
32 . The system of claim 28 , further comprising:
suppressing a non-diseased region from the heatmap image of the pathological slide.
33 . The system of claim 28 , wherein generating a heatmap image for the pathological slide image further comprises:
determining, based on at least one user input, tissue visualization maps to display in the heatmap.
34 . The system of claim 28 , wherein the heatmap image indicates a value for each pixel based on the biomarker expression degree.
35 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, perform operations for analyzing a pathological slide, the operations comprising:
detecting a plurality of tumor cells included in one or more tumor areas from a pathological slide image;
determining a cell expression class of the plurality of tumor cells, based on a biomarker expression degree of the plurality of tumor cells; and
generating a heatmap image for the pathological slide image, based on a result of the determining.
36 . The non-transitory computer-readable medium of claim 35 , wherein the heatmap image indicates scores and/or probabilities for each pixel in the pathological slide image, the scores and/or probabilities being based on the biomarker expression degree.
37 . The non-transitory computer-readable medium of claim 35 , wherein the detecting comprises:
identifying one or more tumor areas and a plurality of tumor cells from the pathological slide image by using an artificial intelligence (AI) model; and
identifying a plurality of tumor cells included in the one or more tumor areas from the plurality of identified tumor cells.
38 . The non-transitory computer-readable medium of claim 37 , wherein the identifying one or more tumor areas comprises identifying a pixel corresponding to the one or more tumor areas by analyzing the pathological slide image in units of pixels.
39 . The non-transitory computer-readable medium of claim 35 , further comprising:
suppressing a non-diseased region from the heatmap image of the pathological slide.
40 . The non-transitory computer-readable medium of claim 35 , wherein generating a heatmap image for the pathological slide image further comprises:
determining, based on at least one user input, tissue visualization maps to display in the heatmap.