Medical image segmentation and severity grading using neural network architectures with semi-supervised learning techniques

A method for providing a computer vision system that analyzes medical images comprising:

• receiving a set of training images including a first subset of training images comprising pixel-level annotation information and a second subset of training images comprising image-level annotation information;
• executing a training procedure that jointly trains a segmentation model to identify medical objects included in medical images and a grading model to assign severity classification labels to the medical images, wherein executing the training procedure includes:
• executing a pre-training procedure that uses a fully-supervised training approach to train the segmentation model with the first subset of training images and the grading model with the second subset of training images;
• generating, using the segmentation model, predicted masks based on the second subset of training images after the pre-training procedure is performed;
• utilizing the predicted masks to train an attention function of the grading model;
• generating, using the attention function of the grading model, pseudo masks based on the second subset of training images;
• and
• utilizing the predicted masks and the pseudo masks to further train the segmentation model using a semi-supervised training approach;
• receiving a medical image;
• and
• generating, using the segmentation model and the grading model, analysis information for the medical image that identifies a severity classification label for the medical image and one or more medical objects included in the medical image.

A computer program product comprising a non-transitory computer-readable medium including instructions for causing a computer to:

• receive a set of training images including a first subset of training images comprising pixel-level annotation information and a second subset of training images comprising image-level annotation information;
• execute a training procedure that jointly trains a segmentation model to identify medical objects included in medical images and a grading model to assign severity classification labels to the medical images, wherein executing the training procedure includes:
• executing a pre-training procedure that uses a fully-supervised training approach to train the segmentation model with the first subset of training images and the grading model with the second subset of training images;
• generating, using the segmentation model, predicted masks based on the second subset of training images after the pre-training procedure is performed;
• utilizing the predicted masks to train an attention function of the grading model;
• generating, using the attention function of the grading model, pseudo masks based on the second subset of training images;
• and
• utilizing the predicted masks and the pseudo masks to further train the segmentation model using a semi-supervised training approach;
• receive a medical image;
• and
• generate, using the segmentation model and the grading model, analysis information for the medical image that identifies a severity classification label for the medical image and one or more medical objects included in the medical image.