Invention Grant
- Patent Title: Systems and methods for training generative adversarial networks and use of trained generative adversarial networks
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Application No.: US16833451Application Date: 2020-03-27
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Publication No.: US11195052B2Publication Date: 2021-12-07
- Inventor: Nhan Ngo Dinh , Giulio Evangelisti , Flavio Navari
- Applicant: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED
- Applicant Address: IE Dublin
- Assignee: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED
- Current Assignee: COSMO ARTIFICIAL INTELLIGENCE—AI LIMITED
- Current Assignee Address: IE Dublin
- Agency: Finnegan, Henderson, Farabow, Garrett & Dunner, LLP
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/62 ; A61B1/00 ; A61B1/273 ; G06N3/08 ; G06T7/00 ; G16H50/20 ; G16H30/40 ; A61B1/31 ; G06N3/04

Abstract:
The present disclosure relates to computer-implemented systems and methods for training and using generative adversarial networks to detect abnormalities in images of a human organ. In one implementation, a method is provided for training a neural network system, the method may include applying a perception branch of an object detection network to frames of a first subset of a plurality of videos to produce a first plurality of detections of abnormalities. Further, the method may include using the first plurality of detections and frames from a second subset of the plurality of videos to train a generator network to generate a plurality of artificial representations of polyps, and training an adversarial branch of the discriminator network to differentiate between artificial representations of the abnormalities and true representations of abnormalities. Additionally, the method may include retraining the perception branch based on difference indicators between the artificial representations of abnormalities and true representations of abnormalities included in frames of the second subset of plurality of videos and a second plurality of detections.
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