Generating training data set for image classification machine learning model

A method for use with a computing system, the method comprising:

• receiving a first labeled image set including a plurality of first images, wherein each of the first images includes one or more first identified regions of interest that have one or more respective first labels;
• receiving an unlabeled image set including a plurality of second images without respective labels;
• identifying a plurality of second identified regions of interest included in the plurality of second images;
• computing a respective feature similarity value between each of the second identified regions of interest and the plurality of first identified regions of interest;
• identifying, in one or more of the second images, a subset of the plurality of the second identified regions of interest that have feature similarity values above a predetermined similarity threshold;
• applying respective second labels to the second identified regions of interest included in the subset;
• constructing a second labeled image set including:
• the one or more second images that include the second identified regions of interest included in the subset;
• and
• the second labels;
• training an image classification machine learning model with a training data set that includes the first labeled image set and the second labeled image set to thereby produce a trained image classification machine learning model;
• during a testing phase, receiving a plurality of test images;
• at the image classification machine learning model, computing a plurality of test labels respectively associated with a plurality of test regions of interest included in the test images;
• computing a model accuracy of the plurality of test labels;
• determining that the model accuracy is below a model accuracy threshold;
• and
• in response to determining that the model accuracy is below the model accuracy threshold, modifying a similarity metric with which the feature similarity values are computed.

A computing system comprising:

• one or more processing devices configured to:
• train an image classification machine learning model using a training data set that includes:
• a plurality of first labeled images that each include one or more first identified regions of interest with one or more respective first labels, wherein the first labeled images are received in one or more markup iterations;
• and
• a plurality of second labeled images that each include one or more second identified regions of interest with one or more respective second labels, wherein the second labels are applied to the second identified regions of interest based at least in part on respective feature similarity values between each of the second identified regions of interest and the plurality of first identified regions of interest;
• during a testing phase, receive a plurality of test images;
• at the image classification machine learning model, compute a plurality of test labels respectively associated with a plurality of test regions of interest included in the test images;
• compute a model accuracy of the plurality of test labels;
• determine that the model accuracy is below a model accuracy threshold;
• in response to determining that the model accuracy is below the model accuracy threshold, modify a similarity metric with which the feature similarity values are computed;
• receive an inferencing-time image;
• at the image classification machine learning model, compute one or more inferencing-time labels respectively associated with one or more inferencing-time regions of interest included in the inferencing-time image;
• and
• output the one or more inferencing-time labels.