Systems and methods to process electronic images to determine histopathology quality
AssigneePaige.AI, Inc.
InventorsEric Robert, George Shaikovski, Christopher Kanan
Life of this patent
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Abstract
A computer-implemented method for processing an electronic image may include receiving, by an artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital whole slide images (WSIs) and extracting one or more vectors of features from one or more foreground tiles of tile images of the one or more digital WSIs. The method may include running a trained machine learning model on the one or more vectors of features and determining, based on an output of the trained machine learning model, whether one or more quality issues are present in the one or more digital WSIs.
Claims20
A computer-implemented method for processing one or more digital whole slide images (WSIs), the method comprising:
- receiving, by a trained artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital WSIs associated with a tissue specimen;
- breaking, by the trained AI system, the one or more digital WSIs into a plurality of tile images;
- determining, by the trained AI system, one or more foreground tiles of the plurality of tile images;
- extracting, by the trained AI system, a plurality of tile feature vectors for the one or more foreground tiles;
- clustering, by the trained AI system, the plurality of tile feature vectors into one or more clusters;
- determining, by the trained AI system, cluster membership for the one or more clusters based on clustering information determined during training of the AI system;
- combining, by the trained AI system, tile feature vectors in associated clusters sharing cluster membership to generate one or more vectors of features representing a slide signature;
- and
- generating, using the trained AI system, an inference of one or more quality issues present in the one or more digital WSIs based on the one or more vectors of features representing the slide signature.
A computer system for processing one or more digital whole slide images (WSIs), the computer system comprising:
- at least one memory storing instructions;
- and
- at least one processor configured to execute the instructions to perform operations comprising:
- receiving, by a trained artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital WSIs associated with a tissue specimen;
- breaking, by the trained AI system, the one or more digital WSIs into a plurality of tile images;
- determining, by the trained AI system, one or more foreground tiles of the plurality of tile images;
- extracting, by the trained AI system, a plurality of tile feature vectors for the one or more foreground tiles;
- clustering, by the trained AI system, the plurality of tile feature vectors into one or more clusters;
- determining, by the trained AI system, cluster membership for the one or more clusters based on clustering information determined during training of the AI system;
- combining, by the trained AI system, tile feature vectors in associated clusters sharing cluster membership to generate one or more vectors of features representing a slide signature;
- and
- generating, using the trained AI system, an inference of one or more quality issues present in the one or more digital WSIs based on the one or more vectors of features representing the slide signature.
A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for processing one or more digital whole slide images (WSIs), the operations comprising:
- receiving, by a trained artificial intelligence (AI) system at an electronic storage of the AI system, one or more digital WSIs associated with a tissue specimen;
- breaking, by the trained AI system, the one or more digital WSIs into a plurality of tile images;
- determining, by the trained AI system, one or more foreground tiles of the plurality of tile images;
- extracting, by the trained AI system, a plurality of tile feature vectors for the one or more foreground tiles;
- clustering, by the trained AI system, the plurality of tile feature vectors into one or more clusters;
- determining, by the trained AI system, cluster membership for the one or more clusters based on clustering information determined during training of the AI system;
- combining, by the trained AI system, tile feature vectors in associated clusters sharing cluster membership to generate one or more vectors of features representing a slide signature;
- and
- generating, using the trained AI system, an inference of one or more quality issues present in the one or more digital WSIs based on the one or more vectors of features representing the slide signature.
Dependencies are inferred from the claim text.