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US Patent 11669965 AI-based label generating system and methods for use therewith

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TimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
Enlitic
Enlitic
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
116699651
Date of Patent
June 6, 2023
1
Patent Application Number
174468631
Date Filed
September 3, 2021
1
Patent Citations
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US Patent 8303505 Methods and apparatuses for image guided medical procedures
1
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US Patent 8331637 System and method of automatic prioritization and analysis of medical images
1
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US Patent 8379950 Medical image processing
1
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US Patent 8885898 Matching of regions of interest across multiple views
1
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US Patent 9165360 Methods, systems, and devices for automated analysis of medical scans
1
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US Patent 9349178 Synthetic data-driven hemodynamic determination in medical imaging
1
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US Patent 9501863 Systems and methods for viewing medical 3D imaging volumes
1
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US Patent 9569736 Intelligent medical image landmark detection
1
...
Patent Primary Examiner
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Michael R Neff
1
CPC Code
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G06V 10/25
1
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G06T 7/0012
1
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G06N 7/005
1
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G06K 9/6256
1
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G06T 7/187
1

A label generating system operates to generate an artificial intelligence model by: training on a training data set that includes the plurality of medical scans with the corresponding global labels; generating testing global probability data by performing an inference function that utilizes the artificial intelligence model on the plurality of medical scans with the corresponding global labels, wherein the testing global probability data indicates a testing set of global probability values corresponding to the set of abnormality classes, and wherein each of the testing set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in each of the plurality of medical scans with the corresponding global labels; comparing the testing set of global probability values to a corresponding confidence threshold for each of the plurality of medical scans selected based on the corresponding one of the global labels; generating an updated training data set by correcting ones of the plurality of medical scans having a corresponding one of the testing set of global probability values that compares unfavorably to the corresponding confidence threshold; and retraining the artificial intelligence model based on the updated training set.

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