Method and system for machine learning classification based on structure or material segmentation in an image

A computer-implemented method for classifying a structure or material in an image of a subject, comprising:

• segmenting an image into one or more segmentations that correspond to respective structures or materials in the image;
• generating from the segmentations one or more segmentation maps of the image including categorizations of pixels or voxels of the segmentation maps assigned from respective predefined sets of categories of the structure or material;
• using a trained classification machine learning model to generate, based on the segmentations maps, one or more classifications and to assign to the classifications respective scores indicative of a likelihood that the structure or material, or the subject, falls into the respective classifications;
• and
• outputting a result indicative of the classifications and scores.

A computer-implemented method for training a classification machine learning model for classifying a structure or material in an image of a subject, the method comprising:

• dividing annotated segmentation maps and annotated non-image data into a training set and a testing set, the annotated segmentation maps obtained by segmenting each of one or more images into one or more segmentations that correspond to respective structures or materials in the respective image;
• (a) implementing a classification machine learning model, including initializing parameters of the classification machine learning model;
• (b) updating the parameters of the classification machine learning model by running a learning algorithm on the training data;
• (c) testing the classification machine learning model on the testing data;
• (d) evaluating whether the classification machine learning model has satisfactory performance;
• repeating steps (a) to (d) when the performance is found in step (d) not to be satisfactory;
• and
• outputting the classification machine learning model for deployment as a trained classification machine learning model, or flagging the classification machine learning model as a trained classification machine learning model.

A system for training a classification machine learning model for classifying a structure or material in an image of a subject, the system comprising a processor configured to:

• divide annotated segmentation maps and annotated non-image data into a training set and a testing set, the annotated segmentation maps obtained by segmenting each of one or more images into one or more segmentations that correspond to respective structures or materials in the respective image;
• (a) implement a classification machine learning model, including initializing parameters of the classification machine learning model;
• (b) update the parameters of the classification machine learning model by running a learning algorithm on the training data;
• (c) test the classification machine learning model on the testing data;
• (d) evaluate whether the classification machine learning model has satisfactory performance;
• repeat steps (a) to (d) when the performance is found in step (d) not to be satisfactory;
• and
• output the classification machine learning model for deployment as a trained classification machine learning model, or flag the classification machine learning model as a trained classification machine learning model.