CPC G06T 3/4038 (2013.01) [G06F 18/2411 (2023.01); G06F 18/2413 (2023.01); G06T 3/4053 (2013.01); G06T 5/50 (2013.01); G06V 10/764 (2022.01); G06V 10/993 (2022.01); G06V 20/693 (2022.01)] | 20 Claims |
1. A method comprising:
obtaining, by a computing system, a training data set comprising a plurality of high confidence super-resolution images and a plurality of low confidence super-resolution images;
generating, by the computing system, a simulated image classifier for determining a confidence interval that measures a likelihood of a super-resolution image falls into a particular class by:
inputting the training data set into a machine learning algorithm; and
learning, via the machine learning algorithm, to classify super-resolution images of artifacts;
obtaining, by the computing system, a target super-resolution image of a specimen;
analyzing, by the computing system via the simulated image classifier, the target super-resolution image to determine a class associated with the target super-resolution image; and
determining, by the computing system via the simulated image classifier, the class of the target super-resolution image and a confidence interval of the class.
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