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OwnerManuel AMTHOR, Daniel HAASEinventors
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A method for assigning image areas of an image series to result classes by means of a processing model that was trained to assign image areas from the image series to a result class. The image series is generated by marking analytes with markers in a plurality of coloring rounds and detecting the markers with a camera. The camera captures or acquires an image of the image series in each coloring round, and the markers are selected in such a way that the signal series of analytes in an image area across the image series include colored signals and uncolored signals. The colored and uncolored signals of the analyte signal series have at least one particular ratio of one of the colored and/or uncolored signals of the respective signal series.
Method for training a machine learning system with a processing model, wherein the processing model is trained to assign a result class to signal series from image areas of an image series, the image series is generated by marking analytes with markers in a plurality of coloring rounds and detects the markers with a camera, the camera acquires an image of the image series in each coloring round, the markers are selected in such a way that the signal series of analytes in an image area across the image series comprise colored signals and uncolored signals, the colored and uncolored signals of the signal series of analytes have at least one particular ratio of one of the colored and/or uncolored signals of the respective signal series to at least one other of the colored and/or uncolored signals of the respective signal series, and/or the signal series of analytes have a characteristic signature comprising the at least one particular ratio, comprising:
Method for training a machine learning system with a candidate extraction model for extracting candidate image series from an image series, wherein the image series is generated by marking analytes with markers in a plurality of coloring rounds and detecting the markers with a camera, the camera captures an image of the image series in each coloring round, the markers are selected in such a way that the images signals of the analyte in an image area across the image series comprise colored and uncolored signals, comprising:
Method for assigning image areas of an image series to result classes by means of an analyte data evaluation system with a processing model, wherein the processing model has been trained to assign a result class to image areas of the image series, the image series is generated by marking analytes with markers in a plurality of coloring rounds and detecting the markers with a camera, the camera captures an image of the image series in each coloring round, the markers are selected such that the signal series of analytes in an image area across the image series comprise colored signals and uncolored signals, the colored and uncolored signals of the signal series of analytes have at least one particular ratio of one of the colored and/or uncolored signals of the respective signal series to at least one other of the colored and/or uncolored signals of the respective signal series and/or the signal series of analytes have a characteristic signature, comprising the at least one particular ratio, the result classes comprise at least one class for each analyte type to be identified, and signal series of analytes each have, for each analyte type to be identified, a specific sequence of the colored and uncolored signals by means of which the respective analyte types can be assigned to the signal series of analytes, comprising:
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