Calibrating output from an image classifier
AssigneeSkin Analytics Ltd
InventorsJack Greenhalgh, Polychronis Kemos, David Bazaga Garcia, Neil Daly
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Abstract
An apparatus for calibrating output from an image classifier. The apparatus has an image classifier trained to compute, from an image, confidence values for each of a plurality of skin conditions potentially depicted in the image. The apparatus has a processor to compute a probability score for at least one skin condition by adjusting an associated confidence value using information from an incidence corrected data set which is a plurality of images resampled according to data about an incidence of the skin condition in a population from which the image was captured.
Claims13
A computer-implemented method of calibrating output from an image classifier, the method comprising the steps of:
- using the image classifier to compute, from an image, confidence values for each of a plurality of skin conditions potentially depicted in the image;
- computing a probability score for each skin condition by adjusting the confidence values using information from an incidence corrected data set which is a plurality of images resampled according to data about an incidence of the skin conditions in a population from which the image was captured so that the incidence corrected data set contains a number of images of each skin condition which reflects incidence of each skin condition in the population,
- wherein the probability score for each skin condition is computed using a neural network to perform regression to predict the probability score for each skin condition from the confidence value for each skin condition, or using a Support Vector Regression (SVR) algorithm trained to predict the probability score for each skin condition from the confidence value for each skin condition;
- and
- sending a selected skin condition of the plurality of skin conditions, the probability score for the selected skin condition, or both, to a display.
An apparatus for calibrating output from an image classifier, the apparatus comprising:
- an image classifier trained to compute, from an image, confidence values for each of a plurality of skin conditions potentially depicted in the image;
- a processor to compute a probability score for each skin condition by adjusting the confidence values using information from an incidence corrected data set which is a plurality of images resampled according to data about an incidence of the skin conditions in a population from which the image was captured so that the incidence corrected data set contains a number of images of each skin condition which reflects incidence of each skin condition in the population, wherein the probability score for each skin condition is computed using a neural network to perform regression to predict the probability score for each skin condition from the confidence value for each skin condition, or using a Support Vector Regression (SVR) algorithm trained to predict the probability score for each skin condition from the confidence value for each skin condition;
- and
- a display operable to display a selected skin condition of the plurality of skin conditions, the probability score for the selected skin condition, or both.
A dermascope imaging device comprising a processor and storage having instructions which when executed by the processor:
- compute, from an image, confidence values for each of a plurality of skin conditions potentially depicted in the image;
- compute a probability score for at least one of the skin conditions by adjusting the confidence value for the skin condition using information from an incidence corrected data set which is a plurality of images resampled according to data about an incidence of the skin condition in a population from which the image was captured so that the incidence corrected data set contains a number of images of each skin condition which reflects incidence of each skin condition in the population,
- wherein the probability score for each skin condition is computed using a neural network to perform regression to predict the probability score for each skin condition from the confidence value for each skin condition, or using a Support Vector Regression (SVR) algorithm trained to predict the probability score for each skin condition from the confidence value for each skin condition;
- and
- send a selected skin condition of the plurality of skin conditions, the probability score for the selected skin condition, or both, to a display.
Dependencies are inferred from the claim text.