Enlitic, Inc.

Ashwin Jadhav

Kevin Lyman

Li Yao

Anthony Upton

Eric C Poblenz

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.

AI-based label generating system and methods for use therewith

A multi-label generating system is configured to: store a first plurality of medical scans with corresponding global labels and a second plurality of medical scans with corresponding region labels, wherein the global labels each correspond to one of a set of abnormality classes and wherein each of the region labels correspond to one of the set of abnormality classes; generate a computer vision model by training on the first plurality of medical scans with the corresponding global labels and the second plurality of medical scans with the corresponding region labels; receive a new medical scan; generate global probability data based on the computer vision model, wherein the global probability data indicates a set of global probability values corresponding to the set of abnormality classes, and wherein each of the set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in the new medical scan; and transmit the global probability data to a client device for display via a display device.

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San Francisco, CA, United States of America

Ashwin Jadhav