Automatic calibration using machine learning

A method for using a cell analysis model, comprising:

• applying a functional dye to a cell to produce a dyed cell, wherein the functional dye is configured to indicate, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic;
• capturing a brightfield image of the dyed cell using brightfield imaging;
• and
• using a model derived by machine learning to determine whether the cell has the given characteristic from the brightfield image,
• wherein the model has been trained with a plurality of fluorescence images and a corresponding plurality of brightfield images, and
• wherein the model is further trained using the plurality of fluorescent images to determine whether the given characteristic is present or not in the corresponding plurality of brightfield images.

A method for creating a cell categorisation model, comprising:

• applying a functional dye to one or more samples comprising a plurality of cells, wherein the functional dye is configured to indicate, during fluorescence imaging and during brightfield imaging, whether each of the cells has a given characteristic;
• capturing a brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the functional dye has been applied;
• and
• using a machine learning process to generate a model that predicts whether a cell has the given characteristic from a brightfield image,
• wherein the model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data, and
• wherein the model is trained using the corresponding fluorescent image to determine whether the given characteristic is present or not in the brightfield image.