Hierarchical workflow for generating annotated training data for machine learning enabled image segmentation
AssigneeGenentech, Inc.
InventorsMiao ZHANG, Nagamurali K. MOVVA, Mahdi ABBASPOUR TEHRANI
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Abstract
Systems and methods for annotating medical images using an artificial intelligence (AI)-enabled workflow are disclosed herein. In some example embodiments, an image of a sample may be labeled using an annotation generated by a neural network The annotation may represent a feature in the image. In some instances, the labeled image may be reviewed by laypersons and/or experts for accuracy and/or completeness, and the labeled image may be updated based on the review to generate an annotated image.
Claims21
A method, comprising:
- receiving an image of a sample having a feature;
- generating, using a neural network, an annotation representing the feature;
- generating, using the neural network, a labeled image comprising the annotation;
- prompting presentation of the labeled image to an image correction interface;
- receiving, from the image correction interface, label correction data related to the annotation generated by the first neural network;
- and
- updating the labeled image using the label correction data to generate an annotated image comprising an update to the labeled image.
A system, comprising:
- a non-transitory memory;
- and
- a hardware processor coupled with the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
- receive an image of a sample having a feature;
- generate, using a neural network, an annotation representing the feature;
- generate, using the neural network, a labeled image comprising the annotation;
- prompt presentation of the labeled image to an image correction interface;
- receive, from the image correction interface, label correction data related to the annotation generated by the first neural network;
- and
- update the labeled image using the label correction data to generate an annotated image comprising an update to the labeled image.
A non-transitory computer-readable medium (CRM) having stored thereon computer-readable instructions executable to cause a computer system to perform operations comprising:
- receiving an image of a sample having a feature;
- generating, using a neural network, an annotation representing the feature;
- generating, using the neural network, a labeled image comprising the annotation;
- prompting presentation of the labeled image to an image correction interface;
- receiving, from the image correction interface, label correction data related to the annotation generated by the first neural network;
- and
- updating the labeled image using the label correction data to generate an annotated image comprising an update to the labeled image.
21-62. (canceled)
Dependencies are inferred from the claim text.